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diff --git a/21.02/_lstm_serialization_tests_8cpp_source.xhtml b/21.02/_lstm_serialization_tests_8cpp_source.xhtml new file mode 100644 index 0000000000..c977d27e0b --- /dev/null +++ b/21.02/_lstm_serialization_tests_8cpp_source.xhtml @@ -0,0 +1,217 @@ +<!-- Copyright (c) 2020 ARM Limited. --> +<!-- --> +<!-- SPDX-License-Identifier: MIT --> +<!-- --> +<!-- HTML header for doxygen 1.8.13--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> +<html xmlns="http://www.w3.org/1999/xhtml"> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.13"/> +<meta name="robots" content="NOINDEX, NOFOLLOW" /> +<meta name="viewport" content="width=device-width, initial-scale=1"/> +<title>ArmNN: src/armnnSerializer/test/LstmSerializationTests.cpp Source File</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="jquery.js"></script> +<script type="text/javascript" src="dynsections.js"></script> +<link href="navtree.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="resize.js"></script> +<script type="text/javascript" src="navtreedata.js"></script> +<script type="text/javascript" src="navtree.js"></script> +<script type="text/javascript"> + $(document).ready(initResizable); +</script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">21.02</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.13 --> +<script type="text/javascript"> +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +$(function() { + initMenu('',true,false,'search.php','Search'); + $(document).ready(function() { init_search(); }); +}); +</script> +<div id="main-nav"></div> +</div><!-- top --> +<div id="side-nav" class="ui-resizable side-nav-resizable"> + <div id="nav-tree"> + <div id="nav-tree-contents"> + <div id="nav-sync" class="sync"></div> + </div> + </div> + <div id="splitbar" style="-moz-user-select:none;" + class="ui-resizable-handle"> + </div> +</div> +<script type="text/javascript"> +$(document).ready(function(){initNavTree('_lstm_serialization_tests_8cpp_source.xhtml','');}); +</script> +<div id="doc-content"> +<!-- window showing the filter options --> +<div id="MSearchSelectWindow" + onmouseover="return searchBox.OnSearchSelectShow()" + onmouseout="return searchBox.OnSearchSelectHide()" + onkeydown="return searchBox.OnSearchSelectKey(event)"> +</div> + +<!-- iframe showing the search results (closed by default) --> +<div id="MSearchResultsWindow"> +<iframe src="javascript:void(0)" frameborder="0" + name="MSearchResults" id="MSearchResults"> +</iframe> +</div> + +<div class="header"> + <div class="headertitle"> +<div class="title">LstmSerializationTests.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_lstm_serialization_tests_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "../Serializer.hpp"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_serializer_test_utils_8hpp.xhtml">SerializerTestUtils.hpp</a>"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_descriptors_8hpp.xhtml">armnn/Descriptors.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_i_runtime_8hpp.xhtml">armnn/IRuntime.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_i_deserializer_8hpp.xhtml">armnnDeserializer/IDeserializer.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_ignore_unused_8hpp.xhtml">armnn/utility/IgnoreUnused.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_lstm_params_8hpp.xhtml">armnn/LstmParams.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_quantized_lstm_params_8hpp.xhtml">armnn/QuantizedLstmParams.hpp</a>></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <boost/test/unit_test.hpp></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include <fmt/format.h></span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <a class="code" href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a>(SerializerTests)</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="keyword">template</span><<span class="keyword">typename</span> Descriptor></div><div class="line"><a name="l00025"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#a0a3344924b451a5e8bdfeaa02d9e7688"> 25</a></span> <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a0a3344924b451a5e8bdfeaa02d9e7688">ConstantVector2LstmInputParams</a>(<span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  Descriptor& descriptor)</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> {</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> lstmInputParams;</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keywordtype">size_t</span> i = 0;</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="comment">// Inserting basic paramters</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &constants[i++];</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &constants[i++];</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &constants[i++];</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &constants[i++];</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &constants[i++];</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &constants[i++];</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &constants[i++];</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &constants[i++];</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &constants[i++];</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordflow">if</span> (!descriptor.m_CifgEnabled)</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  {</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = &constants[i++];</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &constants[i++];</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = &constants[i++];</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  }</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordflow">if</span> (descriptor.m_PeepholeEnabled)</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  {</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordflow">if</span> (!descriptor.m_CifgEnabled)</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  {</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a> = &constants[i++];</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  }</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> = &constants[i++];</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = &constants[i++];</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  }</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">if</span> (descriptor.m_ProjectionEnabled)</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  {</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a> = &constants[i++];</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a> = &constants[i++];</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  }</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordflow">if</span> (descriptor.m_LayerNormEnabled)</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  {</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keywordflow">if</span> (!descriptor.m_CifgEnabled)</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  {</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">m_InputLayerNormWeights</a> = &constants[i++];</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  }</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a> = &constants[i++];</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a> = &constants[i++];</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  lstmInputParams.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a> = &constants[i++];</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  }</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> </div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">return</span> lstmInputParams;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> }</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> <span class="comment">// Works for Lstm and QLstm (QuantizedLstm uses different parameters)</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="keyword">template</span><<span class="keyword">typename</span> Descriptor></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="keyword">class </span>VerifyLstmLayer : <span class="keyword">public</span> <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor</a><Descriptor></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> {</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  VerifyLstmLayer(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keyword">const</span> Descriptor& descriptor,</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a>& inputParams)</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  : <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor<Descriptor></a>(layerName, inputInfos, outputInfos, descriptor)</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  , m_InputParams(inputParams) {}</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordtype">void</span> <a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">ExecuteStrategy</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span> = 0)<span class="keyword"> override</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(constants, <span class="keywordtype">id</span>);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keywordflow">switch</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>())</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  {</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a06b091bc9aea697ba473c71f0bb55925">armnn::LayerType::Lstm</a>:</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  {</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a56e5da77beb8c601e09bf78371b95828">VerifyNameAndConnections</a>(layer, name);</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keyword">const</span> Descriptor& internalDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>Descriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  this-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a05ef6b0820f03499921f103759525a80">VerifyDescriptor</a>(internalDescriptor);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> lstmParams = <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a0a3344924b451a5e8bdfeaa02d9e7688">ConstantVector2LstmInputParams</a>(constants, internalDescriptor);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  VerifyInputParameters(lstmParams);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  }</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a91880b71ea6d007439b7bc7c320b5c25">armnn::LayerType::QLstm</a>:</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  {</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a56e5da77beb8c601e09bf78371b95828">VerifyNameAndConnections</a>(layer, name);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keyword">const</span> Descriptor& internalDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>Descriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  this-><a class="code" href="class_layer_verifier_base_with_descriptor.xhtml#a05ef6b0820f03499921f103759525a80">VerifyDescriptor</a>(internalDescriptor);</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> lstmParams = <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a0a3344924b451a5e8bdfeaa02d9e7688">ConstantVector2LstmInputParams</a>(constants, internalDescriptor);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  VerifyInputParameters(lstmParams);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  }</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  {</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"Unexpected layer type in Lstm test model"</span>);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  }</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  }</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  }</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <span class="keyword">protected</span>:</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordtype">void</span> VerifyInputParameters(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a>& params)</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  {</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="stringliteral">"m_InputToInputWeights"</span>, m_InputParams.m_InputToInputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a>);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="stringliteral">"m_InputToForgetWeights"</span>, m_InputParams.m_InputToForgetWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a>);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="stringliteral">"m_InputToCellWeights"</span>, m_InputParams.m_InputToCellWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a>);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="stringliteral">"m_InputToOutputWeights"</span>, m_InputParams.m_InputToOutputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a>);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="stringliteral">"m_RecurrentToInputWeights"</span>, m_InputParams.m_RecurrentToInputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a>);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="stringliteral">"m_RecurrentToForgetWeights"</span>, m_InputParams.m_RecurrentToForgetWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a>);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="stringliteral">"m_RecurrentToCellWeights"</span>, m_InputParams.m_RecurrentToCellWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a>);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="stringliteral">"m_RecurrentToOutputWeights"</span>, m_InputParams.m_RecurrentToOutputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a>);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="stringliteral">"m_CellToInputWeights"</span>, m_InputParams.m_CellToInputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a>);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="stringliteral">"m_CellToForgetWeights"</span>, m_InputParams.m_CellToForgetWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a>);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="stringliteral">"m_CellToOutputWeights"</span>, m_InputParams.m_CellToOutputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a>);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="stringliteral">"m_InputGateBias"</span>, m_InputParams.m_InputGateBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a>);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="stringliteral">"m_ForgetGateBias"</span>, m_InputParams.m_ForgetGateBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a>);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="stringliteral">"m_CellBias"</span>, m_InputParams.m_CellBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a>);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="stringliteral">"m_OutputGateBias"</span>, m_InputParams.m_OutputGateBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a>);</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="stringliteral">"m_ProjectionWeights"</span>, m_InputParams.m_ProjectionWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a>);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="stringliteral">"m_ProjectionBias"</span>, m_InputParams.m_ProjectionBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a>);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="stringliteral">"m_InputLayerNormWeights"</span>, m_InputParams.m_InputLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">m_InputLayerNormWeights</a>);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="stringliteral">"m_ForgetLayerNormWeights"</span>, m_InputParams.m_ForgetLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a>);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="stringliteral">"m_CellLayerNormWeights"</span>, m_InputParams.m_CellLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a>);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  this-><a class="code" href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">VerifyConstTensors</a>(</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="stringliteral">"m_OutputLayerNormWeights"</span>, m_InputParams.m_OutputLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a>);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  }</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> m_InputParams;</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> };</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> </div><div class="line"><a name="l00178"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960"> 178</a></span> <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeLstmCifgPeepholeNoProjection)</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> {</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a> descriptor;</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0.0f;</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 0.0f;</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">true</span>; <span class="comment">// if this is true then we DON'T need to set the OptCifgParams</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> </div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keyword">const</span> uint32_t batchSize = 1;</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keyword">const</span> uint32_t inputSize = 2;</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keyword">const</span> uint32_t numUnits = 4;</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keyword">const</span> uint32_t outputSize = numUnits;</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> </div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo1({numUnits, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(inputWeightsInfo1, inputToForgetWeightsData);</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(inputWeightsInfo1, inputToCellWeightsData);</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> </div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(inputWeightsInfo1, inputToOutputWeightsData);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo2({numUnits, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(inputWeightsInfo2, recurrentToForgetWeightsData);</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(inputWeightsInfo2, recurrentToCellWeightsData);</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(inputWeightsInfo2, recurrentToOutputWeightsData);</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> </div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo3({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToForgetWeights(inputWeightsInfo3, cellToForgetWeightsData);</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> </div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToOutputWeights(inputWeightsInfo3, cellToOutputWeightsData);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> </div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  std::vector<float> forgetGateBiasData(numUnits, 1.0f);</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(inputWeightsInfo3, forgetGateBiasData);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> </div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  std::vector<float> cellBiasData(numUnits, 0.0f);</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(inputWeightsInfo3, cellBiasData);</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> </div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  std::vector<float> outputGateBiasData(numUnits, 0.0f);</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(inputWeightsInfo3, outputGateBiasData);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> </div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> params;</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &inputToForgetWeights;</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &inputToCellWeights;</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &inputToOutputWeights;</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeights;</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &recurrentToCellWeights;</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeights;</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &forgetGateBias;</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &cellBias;</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &outputGateBias;</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> = &cellToForgetWeights;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = &cellToOutputWeights;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn = network->AddInputLayer(1);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network->AddInputLayer(2);</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"lstm"</span>);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> scratchBuffer = network->AddOutputLayer(0);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut = network->AddOutputLayer(1);</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut = network->AddOutputLayer(2);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(3);</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> </div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="comment">// connect up</span></div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfoScratchBuff({ batchSize, numUnits * 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> </div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> </div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  outputStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  outputStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> </div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  cellStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  cellStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(scratchBuffer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(lstmTensorInfoScratchBuff);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> </div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> </div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> </div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> </div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  BOOST_CHECK(deserializedNetwork);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> </div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  VerifyLstmLayer<armnn::LstmDescriptor> checker(</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  layerName,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  descriptor,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  params);</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  deserializedNetwork->ExecuteStrategy(checker);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> }</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> </div><div class="line"><a name="l00292"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#a884784964d5b3e12dd1a0b76e63a85f9"> 292</a></span> <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeLstmNoCifgWithPeepholeAndProjection)</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> {</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a> descriptor;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0.0f;</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 0.0f;</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">false</span>; <span class="comment">// if this is true then we DON'T need to set the OptCifgParams</span></div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> </div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keyword">const</span> uint32_t batchSize = 2;</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keyword">const</span> uint32_t inputSize = 5;</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keyword">const</span> uint32_t numUnits = 20;</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keyword">const</span> uint32_t outputSize = 16;</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> </div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x5({numUnits, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span> </div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span> </div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span> </div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span> </div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputGateBias(tensorInfo20, inputGateBiasData);</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> </div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(tensorInfo20, cellBiasData);</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span> </div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(tensorInfo20, outputGateBiasData);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span> </div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x16({numUnits, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> </div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span> </div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span> </div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToInputWeights(tensorInfo20, cellToInputWeightsData);</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> </div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> </div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToOutputWeights(tensorInfo20, cellToOutputWeightsData);</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16x20({outputSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionWeights(tensorInfo16x20, projectionWeightsData);</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> </div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  std::vector<float> projectionBiasData(outputSize, 0.f);</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionBias(tensorInfo16, projectionBiasData);</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span> </div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> params;</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &inputToForgetWeights;</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &inputToCellWeights;</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &inputToOutputWeights;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeights;</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &recurrentToCellWeights;</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeights;</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &forgetGateBias;</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &cellBias;</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &outputGateBias;</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span> </div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="comment">// additional params because: descriptor.m_CifgEnabled = false</span></div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = &inputToInputWeights;</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &recurrentToInputWeights;</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a> = &cellToInputWeights;</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = &inputGateBias;</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span> </div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="comment">// additional params because: descriptor.m_ProjectionEnabled = true</span></div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a> = &projectionWeights;</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a> = &projectionBias;</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> </div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <span class="comment">// additional params because: descriptor.m_PeepholeEnabled = true</span></div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> = &cellToForgetWeights;</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = &cellToOutputWeights;</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> </div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn = network->AddInputLayer(1);</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network->AddInputLayer(2);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"lstm"</span>);</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> scratchBuffer = network->AddOutputLayer(0);</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut = network->AddOutputLayer(1);</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut = network->AddOutputLayer(2);</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(3);</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span> </div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="comment">// connect up</span></div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span> </div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  outputStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  outputStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span> </div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  cellStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  cellStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> </div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(scratchBuffer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(lstmTensorInfoScratchBuff);</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span> </div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> </div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span> </div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span> </div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  BOOST_CHECK(deserializedNetwork);</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span> </div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  VerifyLstmLayer<armnn::LstmDescriptor> checker(</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  layerName,</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  descriptor,</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  params);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  deserializedNetwork->ExecuteStrategy(checker);</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span> }</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span> </div><div class="line"><a name="l00438"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#ab6ffd7bf1455358bc87321974530cc58"> 438</a></span> <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeLstmNoCifgWithPeepholeWithProjectionWithLayerNorm)</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span> {</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a> descriptor;</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0.0f;</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 0.0f;</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">false</span>; <span class="comment">// if this is true then we DON'T need to set the OptCifgParams</span></div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span> </div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="keyword">const</span> uint32_t batchSize = 2;</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <span class="keyword">const</span> uint32_t inputSize = 5;</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="keyword">const</span> uint32_t numUnits = 20;</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="keyword">const</span> uint32_t outputSize = 16;</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span> </div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x5({numUnits, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> </div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span> </div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span> </div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span> </div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputGateBias(tensorInfo20, inputGateBiasData);</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span> </div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span> </div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(tensorInfo20, cellBiasData);</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> </div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(tensorInfo20, outputGateBiasData);</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span> </div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x16({numUnits, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span> </div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span> </div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span> </div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span> </div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToInputWeights(tensorInfo20, cellToInputWeightsData);</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span> </div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span> </div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToOutputWeights(tensorInfo20, cellToOutputWeightsData);</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span> </div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16x20({outputSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionWeights(tensorInfo16x20, projectionWeightsData);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span> </div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  std::vector<float> projectionBiasData(outputSize, 0.f);</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionBias(tensorInfo16, projectionBiasData);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span> </div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  std::vector<float> inputLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputLayerNormWeights(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span> </div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  std::vector<float> forgetLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetLayerNormWeights(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> </div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  std::vector<float> cellLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellLayerNormWeights(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> </div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  std::vector<float> outLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outLayerNormWeights(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span> </div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> params;</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &inputToForgetWeights;</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &inputToCellWeights;</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &inputToOutputWeights;</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeights;</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &recurrentToCellWeights;</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeights;</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &forgetGateBias;</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &cellBias;</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &outputGateBias;</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span> </div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="comment">// additional params because: descriptor.m_CifgEnabled = false</span></div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = &inputToInputWeights;</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &recurrentToInputWeights;</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a> = &cellToInputWeights;</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = &inputGateBias;</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span> </div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="comment">// additional params because: descriptor.m_ProjectionEnabled = true</span></div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a> = &projectionWeights;</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a> = &projectionBias;</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span> </div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="comment">// additional params because: descriptor.m_PeepholeEnabled = true</span></div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> = &cellToForgetWeights;</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = &cellToOutputWeights;</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span> </div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="comment">// additional params because: despriptor.m_LayerNormEnabled = true</span></div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">m_InputLayerNormWeights</a> = &inputLayerNormWeights;</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a> = &forgetLayerNormWeights;</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a> = &cellLayerNormWeights;</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a> = &outLayerNormWeights;</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> </div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn = network->AddInputLayer(1);</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network->AddInputLayer(2);</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"lstm"</span>);</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> scratchBuffer = network->AddOutputLayer(0);</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut = network->AddOutputLayer(1);</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut = network->AddOutputLayer(2);</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(3);</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span> </div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <span class="comment">// connect up</span></div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span> </div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span> </div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  outputStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  outputStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span> </div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  cellStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  cellStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span> </div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(scratchBuffer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(lstmTensorInfoScratchBuff);</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span> </div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span> </div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span> </div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span> </div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  BOOST_CHECK(deserializedNetwork);</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span> </div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  VerifyLstmLayer<armnn::LstmDescriptor> checker(</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  layerName,</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  descriptor,</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  params);</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  deserializedNetwork->ExecuteStrategy(checker);</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span> }</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span> </div><div class="line"><a name="l00603"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#ab1042b9567ba6e028411498f0387fbbd"> 603</a></span> <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(EnsureLstmLayersBackwardCompatibility)</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span> {</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <span class="comment">// The hex data below is a flat buffer containing a lstm layer with no Cifg, with peephole and projection</span></div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <span class="comment">// enabled. That data was obtained before additional layer normalization parameters where added to the</span></div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="comment">// lstm serializer. That way it can be tested if a lstm model with the old parameter configuration can</span></div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <span class="comment">// still be loaded</span></div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  <span class="keyword">const</span> std::vector<uint8_t> lstmNoCifgWithPeepholeAndProjectionModel =</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  {</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x38, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  0xDC, 0x29, 0x00, 0x00, 0x38, 0x29, 0x00, 0x00, 0xB4, 0x28, 0x00, 0x00, 0x94, 0x01, 0x00, 0x00, 0x3C, 0x01,</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  0x00, 0x00, 0xE0, 0x00, 0x00, 0x00, 0x84, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x70, 0xD6, 0xFF, 0xFF,</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x06, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x88, 0xD7,</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  0xFF, 0xFF, 0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xF6, 0xD6, 0xFF, 0xFF, 0x07, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  0xE8, 0xD7, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xC8, 0xD6, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x5E, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xE0, 0xD7, 0xFF, 0xFF,</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  0x08, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x4E, 0xD7, 0xFF, 0xFF, 0x06, 0x00, 0x00, 0x00, 0x10, 0x00,</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xD8,</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x20, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  0x04, 0x00, 0x00, 0x00, 0xB6, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x38, 0xD8, 0xFF, 0xFF, 0x08, 0x00,</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0xA6, 0xD7, 0xFF, 0xFF, 0x05, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xD8, 0xFF, 0xFF,</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x78, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00,</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  0x00, 0x00, 0x0E, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x16, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  0xFA, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00,</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xD8, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  0x00, 0x00, 0x6C, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x23, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  0x12, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0xE0, 0x25, 0x00, 0x00, 0xD0, 0x25,</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0x00, 0x48, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00,</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  0x10, 0x00, 0x14, 0x00, 0x18, 0x00, 0x1C, 0x00, 0x20, 0x00, 0x24, 0x00, 0x28, 0x00, 0x2C, 0x00, 0x30, 0x00,</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  0x34, 0x00, 0x38, 0x00, 0x3C, 0x00, 0x40, 0x00, 0x44, 0x00, 0x26, 0x00, 0x00, 0x00, 0xC4, 0x23, 0x00, 0x00,</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  0xF8, 0x21, 0x00, 0x00, 0x2C, 0x20, 0x00, 0x00, 0xF0, 0x1A, 0x00, 0x00, 0xB4, 0x15, 0x00, 0x00, 0x78, 0x10,</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  0x00, 0x00, 0xF0, 0x0F, 0x00, 0x00, 0x68, 0x0F, 0x00, 0x00, 0xE0, 0x0E, 0x00, 0x00, 0x14, 0x0D, 0x00, 0x00,</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  0xD8, 0x07, 0x00, 0x00, 0x50, 0x07, 0x00, 0x00, 0xC8, 0x06, 0x00, 0x00, 0x8C, 0x01, 0x00, 0x00, 0x14, 0x01,</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  0x00, 0x00, 0x8C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xEE, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03,</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00,</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01,</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x72, 0xD8,</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x82, 0xD9, 0xFF, 0xFF,</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDE, 0xD8,</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  0x14, 0x00, 0x00, 0x00, 0xF6, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x54, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  0x00, 0x00, 0x06, 0xDA, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xD9, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x6A, 0xD9, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  0x00, 0x03, 0x14, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x7A, 0xDA, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  0x40, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x86, 0xDE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0xA2, 0xDE,</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xB2, 0xDF, 0xFF, 0xFF,</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xDF,</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  0x14, 0x00, 0x00, 0x00, 0x26, 0xDF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x64, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  0x00, 0x00, 0x36, 0xE0, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  0x00, 0x00, 0x00, 0x00, 0x92, 0xDF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0xAA, 0xDF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03,</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  0x14, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xBA, 0xE0, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x40, 0x01,</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  0x00, 0x00, 0x00, 0x00, 0xC6, 0xE4, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0xE2, 0xE4, 0xFF, 0xFF,</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  0x00, 0x00, 0x00, 0x03, 0xA4, 0x01, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xF2, 0xE5, 0xFF, 0xFF, 0x04, 0x00,</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  0x00, 0x00, 0x64, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xE6, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01,</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x05, 0x00,</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  0x00, 0x00, 0xAA, 0xE6, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  0xBA, 0xE7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  0x00, 0x00, 0x16, 0xE7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x2E, 0xE7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x64, 0x00,</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x3E, 0xE8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9A, 0xE7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0xB2, 0xE7, 0xFF, 0xFF,</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  0x00, 0x00, 0x00, 0x03, 0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xC2, 0xE8, 0xFF, 0xFF, 0x04, 0x00,</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0xE8, 0xFF, 0xFF,</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00,</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  0x00, 0x00, 0x36, 0xE8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x14, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  0x46, 0xE9, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x40, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xED, 0xFF, 0xFF,</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00,</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x6E, 0xED, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x14, 0x05, 0x00, 0x00,</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  0x04, 0x00, 0x00, 0x00, 0x7E, 0xEE, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x40, 0x01, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  0x8A, 0xF2, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0xA6, 0xF2, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03,</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  0x14, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xB6, 0xF3, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x40, 0x01,</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  0x00, 0x00, 0x00, 0x00, 0xC2, 0xF7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0xDE, 0xF7, 0xFF, 0xFF,</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  0x00, 0x00, 0x00, 0x03, 0xA4, 0x01, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xEE, 0xF8, 0xFF, 0xFF, 0x04, 0x00,</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  0x00, 0x00, 0x64, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xF9, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01,</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x05, 0x00,</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  0x00, 0x00, 0xA6, 0xF9, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0xA4, 0x01, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  0xB6, 0xFA, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x64, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFB,</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  0x14, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x6E, 0xFB, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0xA4, 0x01,</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x7E, 0xFC, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x64, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  0x00, 0x00, 0x00, 0x00, 0x1A, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x10, 0x00, 0x0C, 0x00,</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x06, 0x00, 0x07, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  0x01, 0x01, 0x04, 0x00, 0x00, 0x00, 0x2E, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  0x22, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6C, 0x73,</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  0x74, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xEC, 0x00, 0x00, 0x00, 0xD0, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  0xB4, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x88, 0x00, 0x00, 0x00, 0x5C, 0x00, 0x00, 0x00, 0x30, 0x00,</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x14, 0xFF, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  0xA6, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x3C, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  0x04, 0x00, 0x00, 0x00, 0xCE, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFF,</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  0xB4, 0xFE, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x1A, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x50, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  0xF0, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  0x7E, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  0x68, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xCE, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  0x08, 0x00, 0x0E, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00,</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  0x08, 0x00, 0x0E, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x01, 0x00,</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00,</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  0x0E, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00,</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00, 0x04, 0x00, 0x06, 0x00,</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>  0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00,</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00, 0x07, 0x00, 0x0C, 0x00,</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  };</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span> </div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork =</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(std::string(lstmNoCifgWithPeepholeAndProjectionModel.begin(),</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>  lstmNoCifgWithPeepholeAndProjectionModel.end()));</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span> </div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  BOOST_CHECK(deserializedNetwork);</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span> </div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  <span class="comment">// generating the same model parameters which where used to serialize the model (Layer norm is not specified)</span></div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a> descriptor;</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0.0f;</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 0.0f;</div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span> </div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  <span class="keyword">const</span> uint32_t batchSize = 2u;</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  <span class="keyword">const</span> uint32_t inputSize = 5u;</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  <span class="keyword">const</span> uint32_t numUnits = 20u;</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  <span class="keyword">const</span> uint32_t outputSize = 16u;</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span> </div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x5({numUnits, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  std::vector<float> inputToInputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span> </div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  std::vector<float> inputToForgetWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span> </div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  std::vector<float> inputToCellWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span> </div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  std::vector<float> inputToOutputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span> </div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>  std::vector<float> inputGateBiasData(tensorInfo20.GetNumElements(), 0.0f);</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputGateBias(tensorInfo20, inputGateBiasData);</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span> </div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  std::vector<float> forgetGateBiasData(tensorInfo20.GetNumElements(), 0.0f);</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span> </div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  std::vector<float> cellBiasData(tensorInfo20.GetNumElements(), 0.0f);</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(tensorInfo20, cellBiasData);</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span> </div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  std::vector<float> outputGateBiasData(tensorInfo20.GetNumElements(), 0.0f);</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(tensorInfo20, outputGateBiasData);</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span> </div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x16({numUnits, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>  std::vector<float> recurrentToInputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span> </div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>  std::vector<float> recurrentToForgetWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span> </div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  std::vector<float> recurrentToCellWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span> </div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  std::vector<float> recurrentToOutputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span> </div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  std::vector<float> cellToInputWeightsData(tensorInfo20.GetNumElements(), 0.0f);</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToInputWeights(tensorInfo20, cellToInputWeightsData);</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span> </div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  std::vector<float> cellToForgetWeightsData(tensorInfo20.GetNumElements(), 0.0f);</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span> </div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  std::vector<float> cellToOutputWeightsData(tensorInfo20.GetNumElements(), 0.0f);</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToOutputWeights(tensorInfo20, cellToOutputWeightsData);</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span> </div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16x20({outputSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>  std::vector<float> projectionWeightsData(tensorInfo16x20.GetNumElements(), 0.0f);</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionWeights(tensorInfo16x20, projectionWeightsData);</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span> </div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  std::vector<float> projectionBiasData(outputSize, 0.0f);</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionBias(tensorInfo16, projectionBiasData);</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span> </div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> params;</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &inputToForgetWeights;</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &inputToCellWeights;</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &inputToOutputWeights;</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeights;</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &recurrentToCellWeights;</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeights;</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &forgetGateBias;</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &cellBias;</div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &outputGateBias;</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span> </div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  <span class="comment">// additional params because: descriptor.m_CifgEnabled = false</span></div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = &inputToInputWeights;</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &recurrentToInputWeights;</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a> = &cellToInputWeights;</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = &inputGateBias;</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span> </div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  <span class="comment">// additional params because: descriptor.m_ProjectionEnabled = true</span></div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a> = &projectionWeights;</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a> = &projectionBias;</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span> </div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>  <span class="comment">// additional params because: descriptor.m_PeepholeEnabled = true</span></div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> = &cellToForgetWeights;</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = &cellToOutputWeights;</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span> </div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"lstm"</span>);</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span> </div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  VerifyLstmLayer<armnn::LstmDescriptor> checker(</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  layerName,</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>  {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>  {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  descriptor,</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>  params);</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>  deserializedNetwork->ExecuteStrategy(checker);</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span> }</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span> </div><div class="line"><a name="l01335"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#a5b74bdb50eae6e911a1eb4c3a311f536"> 1335</a></span> <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a> <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a5b74bdb50eae6e911a1eb4c3a311f536">ConstantsVector2QuantizedLstmInputParams</a>(</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants)</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span> {</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>  <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a> params;</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span> </div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>  <span class="comment">// index for constants vector</span></div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>  <span class="keywordtype">size_t</span> i = 0;</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span> </div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>  <span class="comment">// Get input parameters</span></div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = &constants[i++];</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &constants[i++];</div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &constants[i++];</div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &constants[i++];</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span> </div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &constants[i++];</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &constants[i++];</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &constants[i++];</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &constants[i++];</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span> </div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = &constants[i++];</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &constants[i++];</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &constants[i++];</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &constants[i++];</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span> </div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>  <span class="keywordflow">return</span> params;</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span> }</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span> </div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span> <span class="keyword">class </span>VerifyQuantizedLstmLayer : <span class="keyword">public</span> <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a></div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span> {</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span> </div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span> <span class="keyword">public</span>:</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  VerifyQuantizedLstmLayer(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a>& inputParams)</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  : <a class="code" href="class_layer_verifier_base.xhtml">LayerVerifierBase</a>(layerName, inputInfos, outputInfos), m_InputParams(inputParams) {}</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span> </div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>  <span class="keywordtype">void</span> ExecuteStrategy(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a>& descriptor,</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>  <span class="keyword">const</span> std::vector<armnn::ConstTensor>& constants,</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name,</div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> <span class="keywordtype">id</span> = 0)<span class="keyword"> override</span></div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span> <span class="keyword"> </span>{</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(descriptor, constants, <span class="keywordtype">id</span>);</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  <span class="keywordflow">switch</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>())</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  {</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a>: <span class="keywordflow">break</span>;</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a890a37ff3bfe123414ba7e6f052b49f3">armnn::LayerType::QuantizedLstm</a>:</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>  {</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>  <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a> params = <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a5b74bdb50eae6e911a1eb4c3a311f536">ConstantsVector2QuantizedLstmInputParams</a>(constants);</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  VerifyInputParameters(params);</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span>  }</div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  {</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(fmt::format(<span class="stringliteral">"Unexpected layer type in QuantizedLstm test model:"</span>,</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>()));</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  }</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  }</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  }</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span> </div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span> <span class="keyword">protected</span>:</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>  <span class="keywordtype">void</span> VerifyInputParameters(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a>& params)</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>  {</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>  VerifyConstTensors(<span class="stringliteral">"m_InputToInputWeights"</span>,</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  m_InputParams.m_InputToInputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a>);</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>  VerifyConstTensors(<span class="stringliteral">"m_InputToForgetWeights"</span>,</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>  m_InputParams.m_InputToForgetWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a>);</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  VerifyConstTensors(<span class="stringliteral">"m_InputToCellWeights"</span>,</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  m_InputParams.m_InputToCellWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a>);</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  VerifyConstTensors(<span class="stringliteral">"m_InputToOutputWeights"</span>,</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  m_InputParams.m_InputToOutputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a>);</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>  VerifyConstTensors(<span class="stringliteral">"m_RecurrentToInputWeights"</span>,</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>  m_InputParams.m_RecurrentToInputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a>);</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  VerifyConstTensors(<span class="stringliteral">"m_RecurrentToForgetWeights"</span>,</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  m_InputParams.m_RecurrentToForgetWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a>);</div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>  VerifyConstTensors(<span class="stringliteral">"m_RecurrentToCellWeights"</span>,</div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>  m_InputParams.m_RecurrentToCellWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a>);</div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>  VerifyConstTensors(<span class="stringliteral">"m_RecurrentToOutputWeights"</span>,</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>  m_InputParams.m_RecurrentToOutputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a>);</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>  VerifyConstTensors(<span class="stringliteral">"m_InputGateBias"</span>,</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>  m_InputParams.m_InputGateBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a>);</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>  VerifyConstTensors(<span class="stringliteral">"m_ForgetGateBias"</span>,</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>  m_InputParams.m_ForgetGateBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a>);</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  VerifyConstTensors(<span class="stringliteral">"m_CellBias"</span>,</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  m_InputParams.m_CellBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a>);</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>  VerifyConstTensors(<span class="stringliteral">"m_OutputGateBias"</span>,</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  m_InputParams.m_OutputGateBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a>);</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  }</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span> </div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span> <span class="keyword">private</span>:</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>  <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a> m_InputParams;</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span> };</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span> </div><div class="line"><a name="l01431"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#a1cb7aec5bf87ff679cfd0ee9aa7d41c2"> 1431</a></span> <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeQuantizedLstm)</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span> {</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>  <span class="keyword">const</span> uint32_t batchSize = 1;</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  <span class="keyword">const</span> uint32_t inputSize = 2;</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>  <span class="keyword">const</span> uint32_t numUnits = 4;</div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>  <span class="keyword">const</span> uint32_t outputSize = numUnits;</div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span> </div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>  <span class="comment">// Scale/Offset for input/output, cellState In/Out, weights, bias</span></div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>  <span class="keywordtype">float</span> inputOutputScale = 0.0078125f;</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>  int32_t inputOutputOffset = 128;</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span> </div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  <span class="keywordtype">float</span> cellStateScale = 0.00048828125f;</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>  int32_t cellStateOffset = 0;</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span> </div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>  <span class="keywordtype">float</span> weightsScale = 0.00408021f;</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>  int32_t weightsOffset = 100;</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span> </div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>  <span class="keywordtype">float</span> biasScale = 3.1876640625e-05f;</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>  int32_t biasOffset = 0;</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span> </div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>  <span class="comment">// The shape of weight data is {outputSize, inputSize} = {4, 2}</span></div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToInputWeightsShape = {4, 2};</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>  std::vector<uint8_t> inputToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputToInputWeightsInfo(inputToInputWeightsShape,</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>  weightsScale,</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>  weightsOffset);</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(inputToInputWeightsInfo, inputToInputWeightsData);</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span> </div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToForgetWeightsShape = {4, 2};</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>  std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputToForgetWeightsInfo(inputToForgetWeightsShape,</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>  weightsScale,</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>  weightsOffset);</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(inputToForgetWeightsInfo, inputToForgetWeightsData);</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span> </div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToCellWeightsShape = {4, 2};</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>  std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputToCellWeightsInfo(inputToCellWeightsShape,</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>  weightsScale,</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>  weightsOffset);</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(inputToCellWeightsInfo, inputToCellWeightsData);</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span> </div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToOutputWeightsShape = {4, 2};</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>  std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputToOutputWeightsInfo(inputToOutputWeightsShape,</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>  weightsScale,</div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>  weightsOffset);</div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(inputToOutputWeightsInfo, inputToOutputWeightsData);</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span> </div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span>  <span class="comment">// The shape of recurrent weight data is {outputSize, outputSize} = {4, 4}</span></div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> recurrentToInputWeightsShape = {4, 4};</div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>  std::vector<uint8_t> recurrentToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentToInputWeightsInfo(recurrentToInputWeightsShape,</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>  weightsScale,</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>  weightsOffset);</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToInputWeights(recurrentToInputWeightsInfo, recurrentToInputWeightsData);</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span> </div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> recurrentToForgetWeightsShape = {4, 4};</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>  std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentToForgetWeightsInfo(recurrentToForgetWeightsShape,</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  weightsScale,</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  weightsOffset);</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(recurrentToForgetWeightsInfo, recurrentToForgetWeightsData);</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span> </div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> recurrentToCellWeightsShape = {4, 4};</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>  std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};</div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentToCellWeightsInfo(recurrentToCellWeightsShape,</div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  weightsScale,</div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span>  weightsOffset);</div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(recurrentToCellWeightsInfo, recurrentToCellWeightsData);</div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span> </div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> recurrentToOutputWeightsShape = {4, 4};</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>  std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentToOutputWeightsInfo(recurrentToOutputWeightsShape,</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>  weightsScale,</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>  weightsOffset);</div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(recurrentToOutputWeightsInfo, recurrentToOutputWeightsData);</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span> </div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  <span class="comment">// The shape of bias data is {outputSize} = {4}</span></div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputGateBiasShape = {4};</div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>  std::vector<int32_t> inputGateBiasData = {1, 2, 3, 4};</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputGateBiasInfo(inputGateBiasShape,</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>  biasScale,</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  biasOffset);</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputGateBias(inputGateBiasInfo, inputGateBiasData);</div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span> </div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> forgetGateBiasShape = {4};</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span>  std::vector<int32_t> forgetGateBiasData = {1, 2, 3, 4};</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> forgetGateBiasInfo(forgetGateBiasShape,</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>  biasScale,</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>  biasOffset);</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(forgetGateBiasInfo, forgetGateBiasData);</div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span> </div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> cellBiasShape = {4};</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>  std::vector<int32_t> cellBiasData = {1, 2, 3, 4};</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellBiasInfo(cellBiasShape,</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>  biasScale,</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>  biasOffset);</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(cellBiasInfo, cellBiasData);</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span> </div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> outputGateBiasShape = {4};</div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>  std::vector<int32_t> outputGateBiasData = {1, 2, 3, 4};</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputGateBiasInfo(outputGateBiasShape,</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>  biasScale,</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>  biasOffset);</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(outputGateBiasInfo, outputGateBiasData);</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span> </div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>  <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a> params;</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = &inputToInputWeights;</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &inputToForgetWeights;</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &inputToCellWeights;</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &inputToOutputWeights;</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &recurrentToInputWeights;</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeights;</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &recurrentToCellWeights;</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeights;</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = &inputGateBias;</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &forgetGateBias;</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &cellBias;</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &outputGateBias;</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span> </div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn = network->AddInputLayer(1);</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network->AddInputLayer(2);</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"QuantizedLstm"</span>);</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> quantizedLstmLayer = network->AddQuantizedLstmLayer(params, layerName.c_str());</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut = network->AddOutputLayer(0);</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(1);</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span> </div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>  <span class="comment">// Connect up</span></div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize },</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>  inputOutputScale,</div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>  inputOutputOffset);</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits },</div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>  cellStateScale,</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>  cellStateOffset);</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize },</div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>  inputOutputScale,</div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>  inputOutputOffset);</div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span> </div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizedLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span> </div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>  cellStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizedLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  cellStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span> </div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>  outputStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizedLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  outputStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span> </div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>  quantizedLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  quantizedLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span> </div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>  quantizedLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  quantizedLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span> </div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span>  BOOST_CHECK(deserializedNetwork);</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span> </div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>  VerifyQuantizedLstmLayer checker(layerName,</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span>  {inputTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>  {cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>  params);</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span> </div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>  deserializedNetwork->ExecuteStrategy(checker);</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span> }</div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span> </div><div class="line"><a name="l01613"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#abc688c3d986dd56e23ecb486712596d0"> 1613</a></span> <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeQLstmBasic)</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span> {</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>  <a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml">armnn::QLstmDescriptor</a> descriptor;</div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span> </div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span> </div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ac81fb0e66dc623dc37c77f219f53a6d3">m_CellClip</a> = 0.0f;</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa6a518b65088f34803b3214334bdff61">m_ProjectionClip</a> = 0.0f;</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span> </div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a09e1f097944f61cc901240f9300364cf">m_InputIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#afec7f36158448f723b426a9527acb189">m_ForgetIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a0477ee1b44ace6090119178eea78cb0b">m_CellIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa43409f9b457352c95c89f20ce5d844d">m_OutputIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span> </div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#af8f724af7210b52529216feefa993c98">m_HiddenStateScale</a> = 0.07f;</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4556cbd764d4848d8ad0637a9eed580d">m_HiddenStateZeroPoint</a> = 0;</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span> </div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numBatches = 2;</div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 5;</div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 4;</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numUnits = 4;</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span> </div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>  <span class="comment">// Scale/Offset quantization info</span></div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>  <span class="keywordtype">float</span> inputScale = 0.0078f;</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>  int32_t inputOffset = 0;</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span> </div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span>  <span class="keywordtype">float</span> outputScale = 0.0078f;</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>  int32_t outputOffset = 0;</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span> </div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>  <span class="keywordtype">float</span> cellStateScale = 3.5002e-05f;</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span>  int32_t cellStateOffset = 0;</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span> </div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>  <span class="keywordtype">float</span> weightsScale = 0.007f;</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>  int32_t weightsOffset = 0;</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span> </div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>  <span class="keywordtype">float</span> biasScale = 3.5002e-05f / 1024;</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  int32_t biasOffset = 0;</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span> </div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>  <span class="comment">// Weights and bias tensor and quantization info</span></div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo({numUnits, inputSize},</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>,</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>  weightsScale,</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>  weightsOffset);</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span> </div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentWeightsInfo({numUnits, outputSize},</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>,</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span>  weightsScale,</div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span>  weightsOffset);</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span> </div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasInfo({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>, biasScale, biasOffset);</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span> </div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>  std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>  std::vector<int8_t> inputToCellWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>  std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span> </div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);</div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);</div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);</div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span> </div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>  std::vector<int8_t> recurrentToForgetWeightsData =</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>  GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>  std::vector<int8_t> recurrentToCellWeightsData =</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span>  GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>  std::vector<int8_t> recurrentToOutputWeightsData =</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>  GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span> </div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span> </div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>  std::vector<int32_t> forgetGateBiasData(numUnits, 1);</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>  std::vector<int32_t> cellBiasData(numUnits, 0);</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>  std::vector<int32_t> outputGateBiasData(numUnits, 0);</div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span> </div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(biasInfo, forgetGateBiasData);</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(biasInfo, cellBiasData);</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(biasInfo, outputGateBiasData);</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span> </div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>  <span class="comment">// Set up params</span></div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> params;</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &inputToForgetWeights;</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &inputToCellWeights;</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &inputToOutputWeights;</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span> </div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeights;</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &recurrentToCellWeights;</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeights;</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span> </div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &forgetGateBias;</div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &cellBias;</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &outputGateBias;</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span> </div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span>  <span class="comment">// Create network</span></div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"qLstm"</span>);</div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span> </div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> input = network->AddInputLayer(0);</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network->AddInputLayer(1);</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn = network->AddInputLayer(2);</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span> </div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span> </div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut = network->AddOutputLayer(0);</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut = network->AddOutputLayer(1);</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(2);</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span> </div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>  <span class="comment">// Input/Output tensor info</span></div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({numBatches , inputSize},</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>  inputScale,</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>  inputOffset);</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span> </div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInfo({numBatches , numUnits},</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  cellStateScale,</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>  cellStateOffset);</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span> </div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInfo({numBatches , outputSize},</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>  outputScale,</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>  outputOffset);</div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span> </div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>  <span class="comment">// Connect input/output slots</span></div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span> </div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>  outputStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>  outputStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateInfo);</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span> </div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>  cellStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>  cellStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span> </div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span> </div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateInfo);</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span> </div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span> </div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>  BOOST_CHECK(deserializedNetwork);</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span> </div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>  VerifyLstmLayer<armnn::QLstmDescriptor> checker(</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>  layerName,</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>  {inputInfo, cellStateInfo, outputStateInfo},</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>  {outputStateInfo, cellStateInfo, outputStateInfo},</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>  descriptor,</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>  params);</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span> </div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>  deserializedNetwork->ExecuteStrategy(checker);</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span> }</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span> </div><div class="line"><a name="l01770"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#ac6d8113d32eed6458ee490bd3de911d5"> 1770</a></span> <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeQLstmCifgLayerNorm)</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span> {</div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>  <a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml">armnn::QLstmDescriptor</a> descriptor;</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span> </div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>  <span class="comment">// CIFG params are used when CIFG is disabled</span></div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span> </div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ac81fb0e66dc623dc37c77f219f53a6d3">m_CellClip</a> = 0.0f;</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa6a518b65088f34803b3214334bdff61">m_ProjectionClip</a> = 0.0f;</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span> </div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a09e1f097944f61cc901240f9300364cf">m_InputIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#afec7f36158448f723b426a9527acb189">m_ForgetIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a0477ee1b44ace6090119178eea78cb0b">m_CellIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa43409f9b457352c95c89f20ce5d844d">m_OutputIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span> </div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#af8f724af7210b52529216feefa993c98">m_HiddenStateScale</a> = 0.07f;</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4556cbd764d4848d8ad0637a9eed580d">m_HiddenStateZeroPoint</a> = 0;</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span> </div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numBatches = 2;</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 5;</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 4;</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numUnits = 4;</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span> </div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>  <span class="comment">// Scale/Offset quantization info</span></div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>  <span class="keywordtype">float</span> inputScale = 0.0078f;</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>  int32_t inputOffset = 0;</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span> </div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>  <span class="keywordtype">float</span> outputScale = 0.0078f;</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>  int32_t outputOffset = 0;</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span> </div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span>  <span class="keywordtype">float</span> cellStateScale = 3.5002e-05f;</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span>  int32_t cellStateOffset = 0;</div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span> </div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>  <span class="keywordtype">float</span> weightsScale = 0.007f;</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>  int32_t weightsOffset = 0;</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span> </div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>  <span class="keywordtype">float</span> layerNormScale = 3.5002e-05f;</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>  int32_t layerNormOffset = 0;</div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span> </div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>  <span class="keywordtype">float</span> biasScale = layerNormScale / 1024;</div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span>  int32_t biasOffset = 0;</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span> </div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>  <span class="comment">// Weights and bias tensor and quantization info</span></div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo({numUnits, inputSize},</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>,</div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>  weightsScale,</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>  weightsOffset);</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span> </div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentWeightsInfo({numUnits, outputSize},</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>,</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>  weightsScale,</div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>  weightsOffset);</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span> </div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasInfo({numUnits},</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>  biasScale,</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span>  biasOffset);</div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span> </div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> layerNormWeightsInfo({numUnits},</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>  layerNormScale,</div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  layerNormOffset);</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span> </div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>  <span class="comment">// Mandatory params</span></div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>  std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>  std::vector<int8_t> inputToCellWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>  std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span> </div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);</div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);</div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);</div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span> </div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span>  std::vector<int8_t> recurrentToForgetWeightsData =</div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>  GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>  std::vector<int8_t> recurrentToCellWeightsData =</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span>  GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>  std::vector<int8_t> recurrentToOutputWeightsData =</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>  GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span> </div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);</div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);</div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span> </div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>  std::vector<int32_t> forgetGateBiasData(numUnits, 1);</div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>  std::vector<int32_t> cellBiasData(numUnits, 0);</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>  std::vector<int32_t> outputGateBiasData(numUnits, 0);</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span> </div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(biasInfo, forgetGateBiasData);</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(biasInfo, cellBiasData);</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(biasInfo, outputGateBiasData);</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span> </div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>  <span class="comment">// Layer Norm</span></div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>  std::vector<int16_t> forgetLayerNormWeightsData =</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>  GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>  std::vector<int16_t> cellLayerNormWeightsData =</div><div class="line"><a name="l01868"></a><span class="lineno"> 1868</span>  GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());</div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span>  std::vector<int16_t> outputLayerNormWeightsData =</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>  GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span> </div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsData);</div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsData);</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsData);</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span> </div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>  <span class="comment">// Set up params</span></div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> params;</div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span> </div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>  <span class="comment">// Mandatory params</span></div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &inputToForgetWeights;</div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &inputToCellWeights;</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &inputToOutputWeights;</div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span> </div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeights;</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &recurrentToCellWeights;</div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeights;</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span> </div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &forgetGateBias;</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &cellBias;</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &outputGateBias;</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span> </div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>  <span class="comment">// Layer Norm</span></div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a> = &forgetLayerNormWeights;</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a> = &cellLayerNormWeights;</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a> = &outputLayerNormWeights;</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span> </div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>  <span class="comment">// Create network</span></div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"qLstm"</span>);</div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span> </div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> input = network->AddInputLayer(0);</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network->AddInputLayer(1);</div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn = network->AddInputLayer(2);</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span> </div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span> </div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut = network->AddOutputLayer(0);</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut = network->AddOutputLayer(1);</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(2);</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span> </div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>  <span class="comment">// Input/Output tensor info</span></div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({numBatches , inputSize},</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>  inputScale,</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>  inputOffset);</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span> </div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInfo({numBatches , numUnits},</div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>  cellStateScale,</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>  cellStateOffset);</div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span> </div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInfo({numBatches , outputSize},</div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>  outputScale,</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>  outputOffset);</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span> </div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>  <span class="comment">// Connect input/output slots</span></div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span> </div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>  outputStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>  outputStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateInfo);</div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span> </div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>  cellStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>  cellStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span> </div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span> </div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateInfo);</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span> </div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span> </div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>  BOOST_CHECK(deserializedNetwork);</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span> </div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>  VerifyLstmLayer<armnn::QLstmDescriptor> checker(layerName,</div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>  {inputInfo, cellStateInfo, outputStateInfo},</div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span>  {outputStateInfo, cellStateInfo, outputStateInfo},</div><div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>  descriptor,</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>  params);</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span> </div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>  deserializedNetwork->ExecuteStrategy(checker);</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span> }</div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span> </div><div class="line"><a name="l01958"></a><span class="lineno"><a class="line" href="_lstm_serialization_tests_8cpp.xhtml#ae4e2b73628cdb905a7b9f6aacae368a0"> 1958</a></span> <a class="code" href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeQLstmAdvanced)</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span> {</div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>  <a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml">armnn::QLstmDescriptor</a> descriptor;</div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span> </div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span> </div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#ac81fb0e66dc623dc37c77f219f53a6d3">m_CellClip</a> = 0.1f;</div><div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa6a518b65088f34803b3214334bdff61">m_ProjectionClip</a> = 0.1f;</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span> </div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a09e1f097944f61cc901240f9300364cf">m_InputIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#afec7f36158448f723b426a9527acb189">m_ForgetIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a0477ee1b44ace6090119178eea78cb0b">m_CellIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa43409f9b457352c95c89f20ce5d844d">m_OutputIntermediateScale</a> = 0.00001f;</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span> </div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#af8f724af7210b52529216feefa993c98">m_HiddenStateScale</a> = 0.07f;</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>  descriptor.<a class="code" href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4556cbd764d4848d8ad0637a9eed580d">m_HiddenStateZeroPoint</a> = 0;</div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span> </div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numBatches = 2;</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 5;</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 4;</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numUnits = 4;</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span> </div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>  <span class="comment">// Scale/Offset quantization info</span></div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span>  <span class="keywordtype">float</span> inputScale = 0.0078f;</div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>  int32_t inputOffset = 0;</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span> </div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>  <span class="keywordtype">float</span> outputScale = 0.0078f;</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>  int32_t outputOffset = 0;</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span> </div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>  <span class="keywordtype">float</span> cellStateScale = 3.5002e-05f;</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>  int32_t cellStateOffset = 0;</div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span> </div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>  <span class="keywordtype">float</span> weightsScale = 0.007f;</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>  int32_t weightsOffset = 0;</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span> </div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>  <span class="keywordtype">float</span> layerNormScale = 3.5002e-05f;</div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>  int32_t layerNormOffset = 0;</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span> </div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>  <span class="keywordtype">float</span> biasScale = layerNormScale / 1024;</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>  int32_t biasOffset = 0;</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span> </div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>  <span class="comment">// Weights and bias tensor and quantization info</span></div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo({numUnits, inputSize},</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>,</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>  weightsScale,</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>  weightsOffset);</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span> </div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentWeightsInfo({numUnits, outputSize},</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>,</div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>  weightsScale,</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>  weightsOffset);</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span> </div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasInfo({numUnits},</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>  biasScale,</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>  biasOffset);</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span> </div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> peepholeWeightsInfo({numUnits},</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>  weightsScale,</div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>  weightsOffset);</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span> </div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> layerNormWeightsInfo({numUnits},</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>  layerNormScale,</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>  layerNormOffset);</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span> </div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> projectionWeightsInfo({outputSize, numUnits},</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>,</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>  weightsScale,</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>  weightsOffset);</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span> </div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>  <span class="comment">// Mandatory params</span></div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>  std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>  std::vector<int8_t> inputToCellWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>  std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span> </div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span> </div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>  std::vector<int8_t> recurrentToForgetWeightsData =</div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span>  GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span>  std::vector<int8_t> recurrentToCellWeightsData =</div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span>  GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>  std::vector<int8_t> recurrentToOutputWeightsData =</div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>  GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span> </div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);</div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span> </div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>  std::vector<int32_t> forgetGateBiasData(numUnits, 1);</div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>  std::vector<int32_t> cellBiasData(numUnits, 0);</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>  std::vector<int32_t> outputGateBiasData(numUnits, 0);</div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span> </div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(biasInfo, forgetGateBiasData);</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(biasInfo, cellBiasData);</div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(biasInfo, outputGateBiasData);</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span> </div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span>  <span class="comment">// CIFG</span></div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span>  std::vector<int8_t> inputToInputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());</div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span>  std::vector<int8_t> recurrentToInputWeightsData =</div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span>  GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());</div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span>  std::vector<int32_t> inputGateBiasData(numUnits, 1);</div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span> </div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(inputWeightsInfo, inputToInputWeightsData);</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToInputWeights(recurrentWeightsInfo, recurrentToInputWeightsData);</div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputGateBias(biasInfo, inputGateBiasData);</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span> </div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>  <span class="comment">// Peephole</span></div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>  std::vector<int16_t> cellToInputWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>  std::vector<int16_t> cellToForgetWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());</div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>  std::vector<int16_t> cellToOutputWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());</div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span> </div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToInputWeights(peepholeWeightsInfo, cellToInputWeightsData);</div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToForgetWeights(peepholeWeightsInfo, cellToForgetWeightsData);</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToOutputWeights(peepholeWeightsInfo, cellToOutputWeightsData);</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span> </div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>  <span class="comment">// Projection</span></div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>  std::vector<int8_t> projectionWeightsData = GenerateRandomData<int8_t>(projectionWeightsInfo.GetNumElements());</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>  std::vector<int32_t> projectionBiasData(outputSize, 1);</div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span> </div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionWeights(projectionWeightsInfo, projectionWeightsData);</div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionBias(biasInfo, projectionBiasData);</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span> </div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span>  <span class="comment">// Layer Norm</span></div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>  std::vector<int16_t> inputLayerNormWeightsData =</div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>  GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());</div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span>  std::vector<int16_t> forgetLayerNormWeightsData =</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>  GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());</div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span>  std::vector<int16_t> cellLayerNormWeightsData =</div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span>  GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());</div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>  std::vector<int16_t> outputLayerNormWeightsData =</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span>  GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span> </div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputLayerNormWeights(layerNormWeightsInfo, inputLayerNormWeightsData);</div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsData);</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsData);</div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsData);</div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span> </div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>  <span class="comment">// Set up params</span></div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> params;</div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span> </div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span>  <span class="comment">// Mandatory params</span></div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &inputToForgetWeights;</div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &inputToCellWeights;</div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &inputToOutputWeights;</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span> </div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeights;</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &recurrentToCellWeights;</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeights;</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span> </div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &forgetGateBias;</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &cellBias;</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &outputGateBias;</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span> </div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>  <span class="comment">// CIFG</span></div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = &inputToInputWeights;</div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &recurrentToInputWeights;</div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = &inputGateBias;</div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span> </div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>  <span class="comment">// Peephole</span></div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a> = &cellToInputWeights;</div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> = &cellToForgetWeights;</div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = &cellToOutputWeights;</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span> </div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>  <span class="comment">// Projection</span></div><div class="line"><a name="l02129"></a><span class="lineno"> 2129</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a> = &projectionWeights;</div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a> = &projectionBias;</div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span> </div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span>  <span class="comment">// Layer Norm</span></div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">m_InputLayerNormWeights</a> = &inputLayerNormWeights;</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a> = &forgetLayerNormWeights;</div><div class="line"><a name="l02135"></a><span class="lineno"> 2135</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a> = &cellLayerNormWeights;</div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a> = &outputLayerNormWeights;</div><div class="line"><a name="l02137"></a><span class="lineno"> 2137</span> </div><div class="line"><a name="l02138"></a><span class="lineno"> 2138</span>  <span class="comment">// Create network</span></div><div class="line"><a name="l02139"></a><span class="lineno"> 2139</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02140"></a><span class="lineno"> 2140</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"qLstm"</span>);</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span> </div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> input = network->AddInputLayer(0);</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network->AddInputLayer(1);</div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn = network->AddInputLayer(2);</div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span> </div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span> </div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut = network->AddOutputLayer(0);</div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut = network->AddOutputLayer(1);</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(2);</div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span> </div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span>  <span class="comment">// Input/Output tensor info</span></div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({numBatches , inputSize},</div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>  inputScale,</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>  inputOffset);</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span> </div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInfo({numBatches , numUnits},</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>  cellStateScale,</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>  cellStateOffset);</div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span> </div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInfo({numBatches , outputSize},</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>,</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span>  outputScale,</div><div class="line"><a name="l02166"></a><span class="lineno"> 2166</span>  outputOffset);</div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span> </div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>  <span class="comment">// Connect input/output slots</span></div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>  input-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span> </div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span>  outputStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>  outputStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateInfo);</div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span> </div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>  cellStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>  cellStateIn-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span> </div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span> </div><div class="line"><a name="l02181"></a><span class="lineno"> 2181</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateInfo);</div><div class="line"><a name="l02183"></a><span class="lineno"> 2183</span> </div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>  qLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateInfo);</div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span> </div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = <a class="code" href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a>(<a class="code" href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a>(*network));</div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span>  BOOST_CHECK(deserializedNetwork);</div><div class="line"><a name="l02189"></a><span class="lineno"> 2189</span> </div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>  VerifyLstmLayer<armnn::QLstmDescriptor> checker(layerName,</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>  {inputInfo, cellStateInfo, outputStateInfo},</div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span>  {outputStateInfo, cellStateInfo, outputStateInfo},</div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>  descriptor,</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>  params);</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span> </div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span>  deserializedNetwork->ExecuteStrategy(checker);</div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span> }</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span> </div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span> <a class="code" href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a>()</div><div class="ttc" id="_output_shape_of_squeeze_8cpp_xhtml_ae3a6cb217a792718f2bd0e8f45e3ca9e"><div class="ttname"><a href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)</div></div> +<div class="ttc" id="_ignore_unused_8hpp_xhtml"><div class="ttname"><a href="_ignore_unused_8hpp.xhtml">IgnoreUnused.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">armnn::LstmDescriptor::m_ProjectionEnabled</a></div><div class="ttdeci">bool m_ProjectionEnabled</div><div class="ttdoc">Enable/disable the projection layer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00945">Descriptors.hpp:945</a></div></div> +<div class="ttc" id="_lstm_serialization_tests_8cpp_xhtml_a0a3344924b451a5e8bdfeaa02d9e7688"><div class="ttname"><a href="_lstm_serialization_tests_8cpp.xhtml#a0a3344924b451a5e8bdfeaa02d9e7688">ConstantVector2LstmInputParams</a></div><div class="ttdeci">armnn::LstmInputParams ConstantVector2LstmInputParams(const std::vector< armnn::ConstTensor > &constants, Descriptor &descriptor)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_serialization_tests_8cpp_source.xhtml#l00025">LstmSerializationTests.cpp:25</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ab03e6e1514f74427916c892f473fe04c"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">armnn::LstmInputParams::m_ProjectionWeights</a></div><div class="ttdeci">const ConstTensor * m_ProjectionWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00055">LstmParams.hpp:55</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a49e11acda22742cbaf6f1b259ead0d84"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">armnn::QuantizedLstmInputParams::m_InputToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00035">QuantizedLstmParams.hpp:35</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00062">INetwork.hpp:62</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a4a9d678146f572808a361dbdc5489f38"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">armnn::LstmInputParams::m_CellBias</a></div><div class="ttdeci">const ConstTensor * m_CellBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00053">LstmParams.hpp:53</a></div></div> +<div class="ttc" id="class_layer_verifier_base_with_descriptor_xhtml"><div class="ttname"><a href="class_layer_verifier_base_with_descriptor.xhtml">LayerVerifierBaseWithDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8hpp_source.xhtml#l00051">SerializerTestUtils.hpp:51</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a4a9d678146f572808a361dbdc5489f38"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">armnn::QuantizedLstmInputParams::m_CellBias</a></div><div class="ttdeci">const ConstTensor * m_CellBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00045">QuantizedLstmParams.hpp:45</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a56b81ca8ba4b4937e0787e4951f043fc"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">armnn::QuantizedLstmInputParams::m_RecurrentToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00041">QuantizedLstmParams.hpp:41</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a86e88bef0df4df96df752b4b8955a3af"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">armnn::LstmDescriptor::m_ClippingThresProj</a></div><div class="ttdeci">float m_ClippingThresProj</div><div class="ttdoc">Clipping threshold value for the projection. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00939">Descriptors.hpp:939</a></div></div> +<div class="ttc" id="_lstm_serialization_tests_8cpp_xhtml_a5b74bdb50eae6e911a1eb4c3a311f536"><div class="ttname"><a href="_lstm_serialization_tests_8cpp.xhtml#a5b74bdb50eae6e911a1eb4c3a311f536">ConstantsVector2QuantizedLstmInputParams</a></div><div class="ttdeci">armnn::QuantizedLstmInputParams ConstantsVector2QuantizedLstmInputParams(const std::vector< armnn::ConstTensor > &constants)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_serialization_tests_8cpp_source.xhtml#l01335">LstmSerializationTests.cpp:1335</a></div></div> +<div class="ttc" id="_serializer_test_utils_8cpp_xhtml_a59d03e40f8f051241e46091cca50d31f"><div class="ttname"><a href="_serializer_test_utils_8cpp.xhtml#a59d03e40f8f051241e46091cca50d31f">DeserializeNetwork</a></div><div class="ttdeci">armnn::INetworkPtr DeserializeNetwork(const std::string &serializerString)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8cpp_source.xhtml#l00146">SerializerTestUtils.cpp:146</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_afe204ca375b74e9a72640c9227918d0a"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">armnn::LstmInputParams::m_CellToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00050">LstmParams.hpp:50</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_ae83131e16df1cace69395a5f99bc5ecb"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">armnn::QuantizedLstmInputParams::m_RecurrentToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00039">QuantizedLstmParams.hpp:39</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div> +<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">armnn::QLstmDescriptor::m_PeepholeEnabled</a></div><div class="ttdeci">bool m_PeepholeEnabled</div><div class="ttdoc">Enable/disable peephole. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01193">Descriptors.hpp:1193</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_ace7a1f1f1041b412b7d8ef82b95ff352"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">armnn::QuantizedLstmInputParams::m_ForgetGateBias</a></div><div class="ttdeci">const ConstTensor * m_ForgetGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00044">QuantizedLstmParams.hpp:44</a></div></div> +<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_af8f724af7210b52529216feefa993c98"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#af8f724af7210b52529216feefa993c98">armnn::QLstmDescriptor::m_HiddenStateScale</a></div><div class="ttdeci">float m_HiddenStateScale</div><div class="ttdoc">Hidden State quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01209">Descriptors.hpp:1209</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a484bafa2f8453a7c5a4a00b92a61b006"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">armnn::LstmInputParams::m_CellToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00048">LstmParams.hpp:48</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a></div></div> +<div class="ttc" id="_quantized_lstm_params_8hpp_xhtml"><div class="ttname"><a href="_quantized_lstm_params_8hpp.xhtml">QuantizedLstmParams.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_aa43409f9b457352c95c89f20ce5d844d"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa43409f9b457352c95c89f20ce5d844d">armnn::QLstmDescriptor::m_OutputIntermediateScale</a></div><div class="ttdeci">float m_OutputIntermediateScale</div><div class="ttdoc">Output intermediate quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01205">Descriptors.hpp:1205</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a9e081a9b94defb30d1558dc912507e0e"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">armnn::LstmInputParams::m_InputGateBias</a></div><div class="ttdeci">const ConstTensor * m_InputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00051">LstmParams.hpp:51</a></div></div> +<div class="ttc" id="_i_runtime_8hpp_xhtml"><div class="ttname"><a href="_i_runtime_8hpp.xhtml">IRuntime.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a9e081a9b94defb30d1558dc912507e0e"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">armnn::QuantizedLstmInputParams::m_InputGateBias</a></div><div class="ttdeci">const ConstTensor * m_InputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00043">QuantizedLstmParams.hpp:43</a></div></div> +<div class="ttc" id="class_layer_verifier_base_with_descriptor_xhtml_a05ef6b0820f03499921f103759525a80"><div class="ttname"><a href="class_layer_verifier_base_with_descriptor.xhtml#a05ef6b0820f03499921f103759525a80">LayerVerifierBaseWithDescriptor::VerifyDescriptor</a></div><div class="ttdeci">void VerifyDescriptor(const Descriptor &descriptor)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8hpp_source.xhtml#l00083">SerializerTestUtils.hpp:83</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a1759754ccb88ecc9af44f3aae6e244ee"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">armnn::LstmInputParams::m_RecurrentToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00046">LstmParams.hpp:46</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div> +<div class="ttc" id="class_layer_verifier_base_with_descriptor_xhtml_a49f7f1098adb86fd2197d9aee3924de2"><div class="ttname"><a href="class_layer_verifier_base_with_descriptor.xhtml#a49f7f1098adb86fd2197d9aee3924de2">LayerVerifierBaseWithDescriptor::ExecuteStrategy</a></div><div class="ttdeci">void ExecuteStrategy(const armnn::IConnectableLayer *layer, const armnn::BaseDescriptor &descriptor, const std::vector< armnn::ConstTensor > &constants, const char *name, const armnn::LayerBindingId id=0) override</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8hpp_source.xhtml#l00061">SerializerTestUtils.hpp:61</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_af0f796fba1a2be9c56b4c9ee534577ee"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">armnn::LstmInputParams::m_ForgetLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_ForgetLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00058">LstmParams.hpp:58</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a35b112e30c3eb153806a2a8c16d178e3"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">armnn::LstmInputParams::m_CellToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00049">LstmParams.hpp:49</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a></div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00013">QuantizedLstmParams.hpp:13</a></div></div> +<div class="ttc" id="_lstm_params_8hpp_xhtml"><div class="ttname"><a href="_lstm_params_8hpp.xhtml">LstmParams.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> +<div class="ttc" id="_lstm_serialization_tests_8cpp_xhtml_a6aa1ada80d8a67ff3212b2dcab708960"><div class="ttname"><a href="_lstm_serialization_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960">BOOST_AUTO_TEST_CASE</a></div><div class="ttdeci">BOOST_AUTO_TEST_CASE(SerializeDeserializeLstmCifgPeepholeNoProjection)</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_serialization_tests_8cpp_source.xhtml#l00178">LstmSerializationTests.cpp:178</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ab8cf8f9fb6792e654c2d8d8382f6f01b"><div class="ttname"><a href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a></div><div class="ttdeci">int LayerBindingId</div><div class="ttdoc">Type of identifiers for bindable layers (inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00210">Types.hpp:210</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a8c0f6d48705f40c5590dde09be262222"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">armnn::LstmInputParams::m_OutputGateBias</a></div><div class="ttdeci">const ConstTensor * m_OutputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00054">LstmParams.hpp:54</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0</div></div> +<div class="ttc" id="structarmnn_1_1_base_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_base_descriptor.xhtml">armnn::BaseDescriptor</a></div><div class="ttdoc">Base class for all descriptors. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00022">Descriptors.hpp:22</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_affcee5f4ab5994a21bee3b78b4e43de3"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">armnn::QuantizedLstmInputParams::m_InputToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00033">QuantizedLstmParams.hpp:33</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a0cd848f65ec31778d708852f0042fe37"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">armnn::LstmInputParams::m_InputLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_InputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00057">LstmParams.hpp:57</a></div></div> +<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a4a8ec49f130084445d44297549254780"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">armnn::QLstmDescriptor::m_LayerNormEnabled</a></div><div class="ttdeci">bool m_LayerNormEnabled</div><div class="ttdoc">Enable/disable layer normalization. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01197">Descriptors.hpp:1197</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a56b81ca8ba4b4937e0787e4951f043fc"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">armnn::LstmInputParams::m_RecurrentToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00047">LstmParams.hpp:47</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a></div><div class="ttdoc">An LstmDescriptor for the LstmLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00911">Descriptors.hpp:911</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a44b0e6b16708df7f0d2bbab141688aaa"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">armnn::LstmInputParams::m_ProjectionBias</a></div><div class="ttdeci">const ConstTensor * m_ProjectionBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00056">LstmParams.hpp:56</a></div></div> +<div class="ttc" id="class_layer_verifier_base_xhtml_a56e5da77beb8c601e09bf78371b95828"><div class="ttname"><a href="class_layer_verifier_base.xhtml#a56e5da77beb8c601e09bf78371b95828">LayerVerifierBase::VerifyNameAndConnections</a></div><div class="ttdeci">void VerifyNameAndConnections(const armnn::IConnectableLayer *layer, const char *name)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8cpp_source.xhtml#l00038">SerializerTestUtils.cpp:38</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a31da1ead6794dd64571afdd0b6efc771"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">armnn::QuantizedLstmInputParams::m_InputToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00034">QuantizedLstmParams.hpp:34</a></div></div> +<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_aa6a518b65088f34803b3214334bdff61"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#aa6a518b65088f34803b3214334bdff61">armnn::QLstmDescriptor::m_ProjectionClip</a></div><div class="ttdeci">float m_ProjectionClip</div><div class="ttdoc">Clipping threshold value for the projection. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01189">Descriptors.hpp:1189</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00314">Tensor.hpp:314</a></div></div> +<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a09e1f097944f61cc901240f9300364cf"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a09e1f097944f61cc901240f9300364cf">armnn::QLstmDescriptor::m_InputIntermediateScale</a></div><div class="ttdeci">float m_InputIntermediateScale</div><div class="ttdoc">Input intermediate quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01199">Descriptors.hpp:1199</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a></div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00013">LstmParams.hpp:13</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a8c0f6d48705f40c5590dde09be262222"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">armnn::QuantizedLstmInputParams::m_OutputGateBias</a></div><div class="ttdeci">const ConstTensor * m_OutputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00046">QuantizedLstmParams.hpp:46</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">armnn::LstmDescriptor::m_PeepholeEnabled</a></div><div class="ttdeci">bool m_PeepholeEnabled</div><div class="ttdoc">Enable/disable peephole. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00943">Descriptors.hpp:943</a></div></div> +<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml">armnn::QLstmDescriptor</a></div><div class="ttdoc">A QLstmDescriptor for the QLstmLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01153">Descriptors.hpp:1153</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_ae1b07ed928036004bd257169e5aeeef4"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">armnn::LstmDescriptor::m_ActivationFunc</a></div><div class="ttdeci">uint32_t m_ActivationFunc</div><div class="ttdoc">The activation function to use. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00935">Descriptors.hpp:935</a></div></div> +<div class="ttc" id="class_layer_verifier_base_xhtml_a9c63da545906a03b453fff6b556ed6ad"><div class="ttname"><a href="class_layer_verifier_base.xhtml#a9c63da545906a03b453fff6b556ed6ad">LayerVerifierBase::VerifyConstTensors</a></div><div class="ttdeci">void VerifyConstTensors(const std::string &tensorName, const armnn::ConstTensor *expectedPtr, const armnn::ConstTensor *actualPtr)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8cpp_source.xhtml#l00071">SerializerTestUtils.cpp:71</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_adceb04ae84c524e4d01881e3754a4d59"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">armnn::IConnectableLayer::GetType</a></div><div class="ttdeci">virtual LayerType GetType() const =0</div><div class="ttdoc">Returns the armnn::LayerType of this layer. </div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a435d3651482bbfcc11263b4e4e0c900f"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">armnn::QuantizedLstmInputParams::m_RecurrentToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00038">QuantizedLstmParams.hpp:38</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a531a3907ec13d3772370da88030191a5"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">armnn::LstmDescriptor::m_ClippingThresCell</a></div><div class="ttdeci">float m_ClippingThresCell</div><div class="ttdoc">Clipping threshold value for the cell state. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00937">Descriptors.hpp:937</a></div></div> +<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_afec7f36158448f723b426a9527acb189"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#afec7f36158448f723b426a9527acb189">armnn::QLstmDescriptor::m_ForgetIntermediateScale</a></div><div class="ttdeci">float m_ForgetIntermediateScale</div><div class="ttdoc">Forget intermediate quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01201">Descriptors.hpp:1201</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ad0b8c32bb5381f4cc999093ba3b98b43"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">armnn::LstmInputParams::m_CellLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_CellLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00059">LstmParams.hpp:59</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ace7a1f1f1041b412b7d8ef82b95ff352"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">armnn::LstmInputParams::m_ForgetGateBias</a></div><div class="ttdeci">const ConstTensor * m_ForgetGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00052">LstmParams.hpp:52</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a49e11acda22742cbaf6f1b259ead0d84"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">armnn::LstmInputParams::m_InputToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00042">LstmParams.hpp:42</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a6e30c7b3451da3ea9cf4259fb602e6e6"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">armnn::LstmInputParams::m_InputToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00043">LstmParams.hpp:43</a></div></div> +<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_ac81fb0e66dc623dc37c77f219f53a6d3"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#ac81fb0e66dc623dc37c77f219f53a6d3">armnn::QLstmDescriptor::m_CellClip</a></div><div class="ttdeci">float m_CellClip</div><div class="ttdoc">Clipping threshold value for the cell state. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01187">Descriptors.hpp:1187</a></div></div> +<div class="ttc" id="_profiler_tests_8cpp_xhtml_af7f71af5c6c124222dd1c42c5df892f4"><div class="ttname"><a href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE_END()</div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">armnn::LstmDescriptor::m_CifgEnabled</a></div><div class="ttdeci">bool m_CifgEnabled</div><div class="ttdoc">Enable/disable cifg (coupled input & forget gate). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00941">Descriptors.hpp:941</a></div></div> +<div class="ttc" id="_serializer_test_utils_8hpp_xhtml"><div class="ttname"><a href="_serializer_test_utils_8hpp.xhtml">SerializerTestUtils.hpp</a></div></div> +<div class="ttc" id="_i_deserializer_8hpp_xhtml"><div class="ttname"><a href="_i_deserializer_8hpp.xhtml">IDeserializer.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ae83131e16df1cace69395a5f99bc5ecb"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">armnn::LstmInputParams::m_RecurrentToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00045">LstmParams.hpp:45</a></div></div> +<div class="ttc" id="classarmnn_1_1_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_exception.xhtml">armnn::Exception</a></div><div class="ttdoc">Base class for all ArmNN exceptions so that users can filter to just those. </div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00046">Exceptions.hpp:46</a></div></div> +<div class="ttc" id="_descriptors_8hpp_xhtml"><div class="ttname"><a href="_descriptors_8hpp.xhtml">Descriptors.hpp</a></div></div> +<div class="ttc" id="class_layer_verifier_base_xhtml"><div class="ttname"><a href="class_layer_verifier_base.xhtml">LayerVerifierBase</a></div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8hpp_source.xhtml#l00024">SerializerTestUtils.hpp:24</a></div></div> +<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">armnn::QLstmDescriptor::m_ProjectionEnabled</a></div><div class="ttdeci">bool m_ProjectionEnabled</div><div class="ttdoc">Enable/disable the projection layer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01195">Descriptors.hpp:1195</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a890a37ff3bfe123414ba7e6f052b49f3"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a890a37ff3bfe123414ba7e6f052b49f3">armnn::LayerType::QuantizedLstm</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a1759754ccb88ecc9af44f3aae6e244ee"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">armnn::QuantizedLstmInputParams::m_RecurrentToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00040">QuantizedLstmParams.hpp:40</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a435d3651482bbfcc11263b4e4e0c900f"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">armnn::LstmInputParams::m_RecurrentToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00044">LstmParams.hpp:44</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a6e30c7b3451da3ea9cf4259fb602e6e6"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">armnn::QuantizedLstmInputParams::m_InputToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00036">QuantizedLstmParams.hpp:36</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a06b091bc9aea697ba473c71f0bb55925"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a06b091bc9aea697ba473c71f0bb55925">armnn::LayerType::Lstm</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a4a8ec49f130084445d44297549254780"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">armnn::LstmDescriptor::m_LayerNormEnabled</a></div><div class="ttdeci">bool m_LayerNormEnabled</div><div class="ttdoc">Enable/disable layer normalization. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00947">Descriptors.hpp:947</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_afcc1c3a20bd2860e0ddd21674389246f"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">armnn::IConnectableLayer::GetName</a></div><div class="ttdeci">virtual const char * GetName() const =0</div><div class="ttdoc">Returns the name of the layer. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00173">INetwork.hpp:173</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &destination)=0</div></div> +<div class="ttc" id="_serializer_test_utils_8cpp_xhtml_a228162aa622e2e39abb4f498c761ab5e"><div class="ttname"><a href="_serializer_test_utils_8cpp.xhtml#a228162aa622e2e39abb4f498c761ab5e">SerializeNetwork</a></div><div class="ttdeci">std::string SerializeNetwork(const armnn::INetwork &network)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_test_utils_8cpp_source.xhtml#l00152">SerializerTestUtils.cpp:152</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a9b18daea2e9f42386055326fd016519a"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">armnn::LstmInputParams::m_OutputLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_OutputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00060">LstmParams.hpp:60</a></div></div> +<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a0477ee1b44ace6090119178eea78cb0b"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a0477ee1b44ace6090119178eea78cb0b">armnn::QLstmDescriptor::m_CellIntermediateScale</a></div><div class="ttdeci">float m_CellIntermediateScale</div><div class="ttdoc">Cell intermediate quantization scale. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01203">Descriptors.hpp:1203</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a464f0ff87b1aabf71febaa71321dd40b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(NetworkOptions networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00510">Network.cpp:510</a></div></div> +<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">armnn::QLstmDescriptor::m_CifgEnabled</a></div><div class="ttdeci">bool m_CifgEnabled</div><div class="ttdoc">Enable/disable CIFG (coupled input & forget gate). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01191">Descriptors.hpp:1191</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a31da1ead6794dd64571afdd0b6efc771"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">armnn::LstmInputParams::m_InputToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00041">LstmParams.hpp:41</a></div></div> +<div class="ttc" id="structarmnn_1_1_q_lstm_descriptor_xhtml_a4556cbd764d4848d8ad0637a9eed580d"><div class="ttname"><a href="structarmnn_1_1_q_lstm_descriptor.xhtml#a4556cbd764d4848d8ad0637a9eed580d">armnn::QLstmDescriptor::m_HiddenStateZeroPoint</a></div><div class="ttdeci">int32_t m_HiddenStateZeroPoint</div><div class="ttdoc">Hidden State zero point. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01207">Descriptors.hpp:1207</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a91880b71ea6d007439b7bc7c320b5c25"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a91880b71ea6d007439b7bc7c320b5c25">armnn::LayerType::QLstm</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_affcee5f4ab5994a21bee3b78b4e43de3"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">armnn::LstmInputParams::m_InputToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00040">LstmParams.hpp:40</a></div></div> +</div><!-- fragment --></div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_bff0d59bec81fb3d13742442d8f4421d.xhtml">armnnSerializer</a></li><li class="navelem"><a class="el" href="dir_fa9774f03679f86fc845ac51a8a81eba.xhtml">test</a></li><li class="navelem"><a class="el" href="_lstm_serialization_tests_8cpp.xhtml">LstmSerializationTests.cpp</a></li> + <li class="footer">Generated on Thu Feb 25 2021 17:27:50 for ArmNN by + <a href="http://www.doxygen.org/index.html"> + <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li> + </ul> +</div> +</body> +</html> |