diff --git a/latest/_arm_compute_tensor_utils_8cpp_source.html b/latest/_arm_compute_tensor_utils_8cpp_source.html
index 5cdaf29..db8bd47 100644
--- a/latest/_arm_compute_tensor_utils_8cpp_source.html
+++ b/latest/_arm_compute_tensor_utils_8cpp_source.html
@@ -36,7 +36,7 @@
   <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 15rem; margin-top: .5rem; margin-left 13px"/>
   <td id="projectalign" style="padding-left: 0.9em;">
    <div id="projectname">
-   &#160;<span id="projectnumber">24.02</span>
+   &#160;<span id="projectnumber">24.05</span>
    </div>
   </td>
  </tr>
@@ -100,48 +100,48 @@
 <div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// Copyright © 2017-2024 Arm Ltd and Contributors. All rights reserved.</span></div>
 <div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment">// SPDX-License-Identifier: MIT</span></div>
 <div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment">//</span></div>
-<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_arm_compute_tensor_utils_8hpp.html">aclCommon/ArmComputeTensorUtils.hpp</a>&gt;</span></div>
-<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_arm_compute_utils_8hpp.html">aclCommon/ArmComputeUtils.hpp</a>&gt;</span></div>
-<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160; </div>
-<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_exceptions_8hpp.html">armnn/Exceptions.hpp</a>&quot;</span></div>
-<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_arm_compute_utils_8hpp.html">ArmComputeUtils.hpp</a>&quot;</span></div>
-<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_descriptors_8hpp.html">armnn/Descriptors.hpp</a>&gt;</span></div>
-<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160; </div>
-<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="preprocessor">#include &lt;fmt/format.h&gt;</span></div>
-<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160; </div>
-<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></div>
-<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;{</div>
-<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="keyword">namespace </span>armcomputetensorutils</div>
-<div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;{</div>
-<div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160; </div>
-<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> GetArmComputeDataType(<a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> dataType, <span class="keywordtype">bool</span> multiScales)</div>
-<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;{</div>
-<div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;    <span class="keywordflow">switch</span>(dataType)</div>
-<div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;    {</div>
-<div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>:</div>
-<div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::BFLOAT16;</div>
-<div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>:</div>
-<div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::U8;</div>
-<div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>:</div>
-<div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::F16;</div>
-<div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div>
-<div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::F32;</div>
-<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>:</div>
-<div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::QASYMM8_SIGNED;</div>
-<div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div>
-<div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::QASYMM8;</div>
-<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>:</div>
-<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::QSYMM16;</div>
-<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a>:</div>
-<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::S64;</div>
-<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>:</div>
-<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;        {</div>
-<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;            <span class="keywordflow">return</span> multiScales ? arm_compute::DataType::QSYMM8_PER_CHANNEL : arm_compute::DataType::QSYMM8;</div>
-<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;        }</div>
-<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>:</div>
-<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::S32;</div>
-<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;        <span class="keywordflow">default</span>:</div>
-<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;            <a class="code" href="_assert_8hpp.html#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, <span class="stringliteral">&quot;Unknown data type&quot;</span>);</div>
+<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160; </div>
+<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_exceptions_8hpp.html">armnn/Exceptions.hpp</a>&gt;</span></div>
+<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_arm_compute_tensor_utils_8hpp.html">aclCommon/ArmComputeTensorUtils.hpp</a>&gt;</span></div>
+<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_arm_compute_utils_8hpp.html">aclCommon/ArmComputeUtils.hpp</a>&gt;</span></div>
+<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160; </div>
+<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_arm_compute_utils_8hpp.html">ArmComputeUtils.hpp</a>&quot;</span></div>
+<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="_descriptors_8hpp.html">armnn/Descriptors.hpp</a>&gt;</span></div>
+<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160; </div>
+<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="preprocessor">#include &lt;fmt/format.h&gt;</span></div>
+<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160; </div>
+<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearmnn.html">armnn</a></div>
+<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;{</div>
+<div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="keyword">namespace </span>armcomputetensorutils</div>
+<div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;{</div>
+<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160; </div>
+<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> GetArmComputeDataType(<a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> dataType, <span class="keywordtype">bool</span> multiScales)</div>
+<div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;{</div>
+<div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;    <span class="keywordflow">switch</span>(dataType)</div>
+<div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;    {</div>
+<div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a>:</div>
+<div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::BFLOAT16;</div>
+<div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>:</div>
+<div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::U8;</div>
+<div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>:</div>
+<div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::F16;</div>
+<div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div>
+<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::F32;</div>
+<div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>:</div>
+<div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::QASYMM8_SIGNED;</div>
+<div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div>
+<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::QASYMM8;</div>
+<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>:</div>
+<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::QSYMM16;</div>
+<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::DataType::Signed64</a>:</div>
+<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::S64;</div>
+<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>:</div>
+<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;        {</div>
+<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;            <span class="keywordflow">return</span> multiScales ? arm_compute::DataType::QSYMM8_PER_CHANNEL : arm_compute::DataType::QSYMM8;</div>
+<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;        }</div>
+<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>:</div>
+<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::S32;</div>
+<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;        <span class="keywordflow">default</span>:</div>
 <div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;            <span class="keywordflow">return</span> arm_compute::DataType::UNKNOWN;</div>
 <div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;    }</div>
 <div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;}</div>
@@ -173,401 +173,428 @@
 <div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::S32:</div>
 <div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;            <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>;</div>
 <div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;        <span class="keywordflow">default</span>:</div>
-<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;            <a class="code" href="_assert_8hpp.html#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(<span class="keyword">false</span>, <span class="stringliteral">&quot;Unknown data type&quot;</span>);</div>
-<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;            <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>;</div>
-<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;    }</div>
-<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;}</div>
-<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160; </div>
-<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;<a class="code" href="namespacearmnn.html#ac6e86c1def7f674d3c4cb7f577874aa6">arm_compute::Coordinates</a> BuildArmComputeReductionCoordinates(<span class="keywordtype">size_t</span> inputDimensions,</div>
-<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;                                                             <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> originalInputRank,</div>
-<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;                                                             <span class="keyword">const</span> std::vector&lt;unsigned int&gt;&amp; armnnAxes)</div>
-<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;{</div>
-<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    <a class="code" href="namespacearmnn.html#ac6e86c1def7f674d3c4cb7f577874aa6">arm_compute::Coordinates</a> outAclCoords;</div>
-<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160; </div>
-<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    <span class="keywordflow">if</span> (armnnAxes.empty())</div>
-<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    {</div>
-<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        <span class="comment">// If no reduction axes were provided, then the input must be reduced along all dimensions.</span></div>
-<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;        <span class="comment">// Since Compute Library does not accept an empty vector as the reduction dimensions, we then</span></div>
-<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;        <span class="comment">// manually create a vector including all the input dimensions (in reversed order) as:</span></div>
-<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;        <span class="comment">//</span></div>
-<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;        <span class="comment">// { inputDimensions - 1, inputDimensions - 2, ..., 1, 0 }</span></div>
-<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;        <span class="comment">//</span></div>
-<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;        outAclCoords.set_num_dimensions(inputDimensions);</div>
-<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;        std::generate(outAclCoords.begin(), outAclCoords.end(), [d = inputDimensions - 1] () <span class="keyword">mutable</span> { return d--; });</div>
-<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    }</div>
-<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    <span class="keywordflow">else</span></div>
-<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    {</div>
-<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;        <span class="comment">// Create a vector of reduction dimensions (in reversed order) with the given reduction axes.</span></div>
-<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;        <span class="comment">//</span></div>
-<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;        <span class="comment">// Adjust the given reduction axes according to the original rank of the input tensor (before ACL applied any</span></div>
-<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;        <span class="comment">// dimension correction).</span></div>
-<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;        <span class="comment">// For example, if the input tensor originally had 4 dimensions, and one of the reduction axes was 2, then the</span></div>
-<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;        <span class="comment">// new value for that reduction axis should be 1.</span></div>
-<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;        <span class="comment">//</span></div>
-<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;        <span class="comment">// Example:</span></div>
-<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;        <span class="comment">// ArmNN input shape = { 1, 1, 3, 2 } -&gt; ACL input shape = { 2, 3 }</span></div>
-<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;        <span class="comment">// ArmNN reduction axis = { 2 }       -&gt; ACL reduction axis = { 1 }</span></div>
-<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;        <span class="comment">// ArmNN reduction axis = { 3 }       -&gt; ACL reduction axis = { 0 }</span></div>
-<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;        <span class="comment">//</span></div>
-<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;        <span class="comment">// The transformation: ACL reduction axis index = original rank - ArmNN reduction axis index - 1</span></div>
-<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;        <span class="comment">//</span></div>
-<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;        outAclCoords.set_num_dimensions(armnnAxes.size());</div>
-<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;        std::transform(armnnAxes.begin(), armnnAxes.end(),</div>
-<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;                       outAclCoords.begin(),</div>
-<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;                       [originalInputRank](<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i){ return originalInputRank - i - 1; });</div>
-<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    }</div>
-<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160; </div>
-<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    <span class="keywordflow">return</span> outAclCoords;</div>
-<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;}</div>
-<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160; </div>
-<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;arm_compute::TensorShape BuildArmComputeTensorShape(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a>&amp; tensorShape)</div>
-<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;{</div>
-<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    arm_compute::TensorShape shape;</div>
-<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160; </div>
-<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    <span class="comment">// armnn tensors are (batch, channels, height, width).</span></div>
-<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;    <span class="comment">// arm_compute tensors are (width, height, channels, batch).</span></div>
-<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++)</div>
-<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    {</div>
-<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;        <span class="comment">// Note that our dimensions are stored in the opposite order to ACL&#39;s.</span></div>
-<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;        shape.set(tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - i - 1, tensorShape[i], <span class="keyword">false</span>);</div>
-<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160; </div>
-<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;        <span class="comment">// TensorShape::set() flattens leading ones, so that batch size 1 cannot happen.</span></div>
-<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;        <span class="comment">// arm_compute tensors expect this.</span></div>
-<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;    }</div>
-<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160; </div>
-<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    <span class="comment">// prevent arm_compute issue where tensor is flattened to nothing</span></div>
-<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;    <span class="keywordflow">if</span> (shape.num_dimensions() == 0)</div>
-<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    {</div>
-<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;        shape.set_num_dimensions(1);</div>
-<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    }</div>
-<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160; </div>
-<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    <span class="keywordflow">return</span> shape;</div>
-<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;}</div>
-<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160; </div>
-<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;std::vector&lt;unsigned int&gt; ReduceDimsForACL(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a> tensorShape, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimensions)</div>
-<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;{</div>
-<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;    std::vector&lt;unsigned int&gt; newShape;</div>
-<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160; </div>
-<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimsToSkip = 0;</div>
-<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160; </div>
-<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    <span class="keywordflow">if</span> (tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() &gt; dimensions)</div>
-<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    {</div>
-<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;        dimsToSkip = tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - dimensions;</div>
-<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    }</div>
-<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimsSkipped = 0;</div>
-<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    <span class="keywordtype">bool</span> insertRemainder = <span class="keyword">false</span>;</div>
-<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160; </div>
-<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</div>
-<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;    {</div>
-<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;        <span class="keywordflow">if</span> (tensorShape[i] == 1 &amp;&amp; dimsSkipped &lt; dimsToSkip &amp;&amp; !insertRemainder)</div>
-<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;        {</div>
-<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;            ++dimsSkipped;</div>
-<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;            <span class="keywordflow">continue</span>;</div>
-<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;        }</div>
-<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;        newShape.insert(newShape.begin(), tensorShape[i]);</div>
-<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;        <span class="comment">// Once we insert the first dimension we can&#39;t skip any more</span></div>
-<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;        insertRemainder = <span class="keyword">true</span>;</div>
-<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;    }</div>
-<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    <span class="keywordflow">return</span> newShape;</div>
-<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;}</div>
-<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160; </div>
-<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;arm_compute::TensorShape BuildArmComputeTensorShape(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a>&amp; tensorShape, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimensions)</div>
-<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;{</div>
-<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    arm_compute::TensorShape shape;</div>
-<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    std::vector&lt;unsigned int&gt; strippedShape = ReduceDimsForACL(tensorShape, dimensions);</div>
-<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160; </div>
-<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; strippedShape.size(); i++)</div>
-<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    {</div>
-<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;        shape.set(i, strippedShape[i], <span class="keyword">false</span>);</div>
-<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    }</div>
-<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160; </div>
-<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;    <span class="comment">// prevent arm_compute issue where tensor is flattened to nothing</span></div>
-<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;    <span class="keywordflow">if</span> (shape.num_dimensions() == 0)</div>
-<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;    {</div>
-<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;        shape.set_num_dimensions(1);</div>
-<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    }</div>
-<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    <span class="keywordflow">return</span> shape;</div>
-<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;}</div>
-<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160; </div>
-<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;<span class="comment">// Utility function used to build a TensorInfo object, that can be used to initialise</span></div>
-<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;<span class="comment">// ARM Compute Tensor and CLTensor allocators.</span></div>
-<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;<span class="comment">// Note: this utility ignores the value of armnn::TensorInfo.IsConstant(). ACL tensors</span></div>
-<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;<span class="comment">// default to constant but Arm NN ones default to non constant. In the cases where</span></div>
-<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;<span class="comment">// we expect ACL to treat a tensor as constant that value must be set after this</span></div>
-<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;<span class="comment">// utility has been called.</span></div>
-<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; tensorInfo)</div>
-<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;{</div>
-<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;    <span class="keywordtype">bool</span> multiScales = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#af672d1c9e2a120a18926cb645981fbb7">HasMultipleQuantizationScales</a>();</div>
-<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    <span class="keyword">const</span> arm_compute::TensorShape aclTensorShape = BuildArmComputeTensorShape(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div>
-<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> aclDataType       = GetArmComputeDataType(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#aea909c7327109228ef618d459015def3">GetDataType</a>(), multiScales);</div>
-<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160; </div>
-<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    <span class="keyword">const</span> arm_compute::QuantizationInfo aclQuantizationInfo = multiScales ?</div>
-<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;        arm_compute::QuantizationInfo(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8bc11f1fa23ef42532f9fdd04d355270">GetQuantizationScales</a>()) :</div>
-<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;        arm_compute::QuantizationInfo(tensorInfo.GetQuantizationScale(), tensorInfo.GetQuantizationOffset());</div>
-<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160; </div>
-<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;    <span class="keywordflow">return</span> arm_compute::TensorInfo(aclTensorShape, 1, aclDataType, aclQuantizationInfo);</div>
-<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;}</div>
-<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160; </div>
-<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; tensorInfo,</div>
-<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;                                                  <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div>
-<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;{</div>
-<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    arm_compute::TensorInfo aclTensorInfo = BuildArmComputeTensorInfo(tensorInfo);</div>
-<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    aclTensorInfo.set_data_layout(ConvertDataLayout(dataLayout));</div>
-<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160; </div>
-<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;    <span class="keywordflow">return</span> aclTensorInfo;</div>
-<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;}</div>
-<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160; </div>
-<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; tensorInfo, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimensions)</div>
-<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;{</div>
-<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;    <span class="keywordtype">bool</span> multiScales = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#af672d1c9e2a120a18926cb645981fbb7">HasMultipleQuantizationScales</a>();</div>
-<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    <span class="keyword">const</span> arm_compute::TensorShape aclTensorShape = BuildArmComputeTensorShape(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), dimensions);</div>
-<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> aclDataType       = GetArmComputeDataType(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#aea909c7327109228ef618d459015def3">GetDataType</a>(), multiScales);</div>
-<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160; </div>
-<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    <span class="keyword">const</span> arm_compute::QuantizationInfo aclQuantizationInfo = multiScales ?</div>
-<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;              arm_compute::QuantizationInfo(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8bc11f1fa23ef42532f9fdd04d355270">GetQuantizationScales</a>()) :</div>
-<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;              arm_compute::QuantizationInfo(tensorInfo.GetQuantizationScale(), tensorInfo.GetQuantizationOffset());</div>
-<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160; </div>
-<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    <span class="keywordflow">return</span> arm_compute::TensorInfo(aclTensorShape, 1, aclDataType, aclQuantizationInfo);</div>
-<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;}</div>
-<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; tensorInfo,</div>
-<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;                                                  <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimensions)</div>
-<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;{</div>
-<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    arm_compute::TensorInfo aclTensorInfo = BuildArmComputeTensorInfo(tensorInfo, dimensions);</div>
-<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;    aclTensorInfo.set_data_layout(ConvertDataLayout(dataLayout));</div>
-<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160; </div>
-<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;    <span class="keywordflow">return</span> aclTensorInfo;</div>
-<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;}</div>
+<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;            <span class="keywordflow">throw</span> InvalidArgumentException(<span class="stringliteral">&quot;Unknown arm_compute::DataType data type&quot;</span>);</div>
+<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    }</div>
+<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;}</div>
+<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160; </div>
+<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;<a class="code" href="namespacearmnn.html#ac6e86c1def7f674d3c4cb7f577874aa6">arm_compute::Coordinates</a> BuildArmComputeReductionCoordinates(<span class="keywordtype">size_t</span> inputDimensions,</div>
+<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;                                                             <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> originalInputRank,</div>
+<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;                                                             <span class="keyword">const</span> std::vector&lt;unsigned int&gt;&amp; armnnAxes)</div>
+<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;{</div>
+<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    <a class="code" href="namespacearmnn.html#ac6e86c1def7f674d3c4cb7f577874aa6">arm_compute::Coordinates</a> outAclCoords;</div>
+<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160; </div>
+<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;    <span class="keywordflow">if</span> (armnnAxes.empty())</div>
+<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    {</div>
+<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;        <span class="comment">// If no reduction axes were provided, then the input must be reduced along all dimensions.</span></div>
+<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        <span class="comment">// Since Compute Library does not accept an empty vector as the reduction dimensions, we then</span></div>
+<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;        <span class="comment">// manually create a vector including all the input dimensions (in reversed order) as:</span></div>
+<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;        <span class="comment">//</span></div>
+<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;        <span class="comment">// { inputDimensions - 1, inputDimensions - 2, ..., 1, 0 }</span></div>
+<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;        <span class="comment">//</span></div>
+<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;        outAclCoords.set_num_dimensions(inputDimensions);</div>
+<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;        std::generate(outAclCoords.begin(), outAclCoords.end(), [d = inputDimensions - 1] () <span class="keyword">mutable</span> { return d--; });</div>
+<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;    }</div>
+<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    {</div>
+<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;        <span class="comment">// Create a vector of reduction dimensions (in reversed order) with the given reduction axes.</span></div>
+<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;        <span class="comment">//</span></div>
+<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;        <span class="comment">// Adjust the given reduction axes according to the original rank of the input tensor (before ACL applied any</span></div>
+<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;        <span class="comment">// dimension correction).</span></div>
+<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;        <span class="comment">// For example, if the input tensor originally had 4 dimensions, and one of the reduction axes was 2, then the</span></div>
+<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;        <span class="comment">// new value for that reduction axis should be 1.</span></div>
+<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;        <span class="comment">//</span></div>
+<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;        <span class="comment">// Example:</span></div>
+<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;        <span class="comment">// ArmNN input shape = { 1, 1, 3, 2 } -&gt; ACL input shape = { 2, 3 }</span></div>
+<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;        <span class="comment">// ArmNN reduction axis = { 2 }       -&gt; ACL reduction axis = { 1 }</span></div>
+<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;        <span class="comment">// ArmNN reduction axis = { 3 }       -&gt; ACL reduction axis = { 0 }</span></div>
+<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;        <span class="comment">//</span></div>
+<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;        <span class="comment">// The transformation: ACL reduction axis index = original rank - ArmNN reduction axis index - 1</span></div>
+<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;        <span class="comment">//</span></div>
+<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;        outAclCoords.set_num_dimensions(armnnAxes.size());</div>
+<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;        std::transform(armnnAxes.begin(), armnnAxes.end(),</div>
+<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;                       outAclCoords.begin(),</div>
+<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;                       [originalInputRank](<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i){ return originalInputRank - i - 1; });</div>
+<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    }</div>
+<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160; </div>
+<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    <span class="keywordflow">return</span> outAclCoords;</div>
+<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;}</div>
+<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160; </div>
+<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;arm_compute::TensorShape BuildArmComputeTensorShape(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a>&amp; tensorShape)</div>
+<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;{</div>
+<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    arm_compute::TensorShape shape;</div>
+<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160; </div>
+<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    <span class="comment">// armnn tensors are (batch, channels, height, width).</span></div>
+<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    <span class="comment">// arm_compute tensors are (width, height, channels, batch).</span></div>
+<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++)</div>
+<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    {</div>
+<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;        <span class="comment">// Note that our dimensions are stored in the opposite order to ACL&#39;s.</span></div>
+<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;        shape.set(tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - i - 1, tensorShape[i], <span class="keyword">false</span>);</div>
+<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160; </div>
+<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;        <span class="comment">// TensorShape::set() flattens leading ones, so that batch size 1 cannot happen.</span></div>
+<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;        <span class="comment">// arm_compute tensors expect this.</span></div>
+<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;    }</div>
+<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160; </div>
+<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    <span class="comment">// prevent arm_compute issue where tensor is flattened to nothing</span></div>
+<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    <span class="keywordflow">if</span> (shape.num_dimensions() == 0)</div>
+<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;    {</div>
+<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;        shape.set_num_dimensions(1);</div>
+<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    }</div>
+<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160; </div>
+<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    <span class="keywordflow">return</span> shape;</div>
+<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;}</div>
+<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160; </div>
+<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;std::vector&lt;unsigned int&gt; ReduceDimsForACL(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a> tensorShape, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimensions)</div>
+<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;{</div>
+<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;    std::vector&lt;unsigned int&gt; newShape;</div>
+<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160; </div>
+<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimsToSkip = 0;</div>
+<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160; </div>
+<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    <span class="keywordflow">if</span> (tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() &gt; dimensions)</div>
+<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    {</div>
+<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;        dimsToSkip = tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - dimensions;</div>
+<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    }</div>
+<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimsSkipped = 0;</div>
+<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    <span class="keywordtype">bool</span> insertRemainder = <span class="keyword">false</span>;</div>
+<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160; </div>
+<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; tensorShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</div>
+<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    {</div>
+<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;        <span class="keywordflow">if</span> (tensorShape[i] == 1 &amp;&amp; dimsSkipped &lt; dimsToSkip &amp;&amp; !insertRemainder)</div>
+<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;        {</div>
+<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;            ++dimsSkipped;</div>
+<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;            <span class="keywordflow">continue</span>;</div>
+<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;        }</div>
+<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;        newShape.insert(newShape.begin(), tensorShape[i]);</div>
+<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;        <span class="comment">// Once we insert the first dimension we can&#39;t skip any more</span></div>
+<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;        insertRemainder = <span class="keyword">true</span>;</div>
+<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    }</div>
+<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;    <span class="keywordflow">return</span> newShape;</div>
+<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;}</div>
+<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160; </div>
+<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;arm_compute::TensorShape BuildArmComputeTensorShape(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a>&amp; tensorShape, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimensions)</div>
+<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;{</div>
+<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;    arm_compute::TensorShape shape;</div>
+<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    std::vector&lt;unsigned int&gt; strippedShape = ReduceDimsForACL(tensorShape, dimensions);</div>
+<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160; </div>
+<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; strippedShape.size(); i++)</div>
+<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    {</div>
+<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;        shape.set(i, strippedShape[i], <span class="keyword">false</span>);</div>
+<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    }</div>
+<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160; </div>
+<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    <span class="comment">// prevent arm_compute issue where tensor is flattened to nothing</span></div>
+<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;    <span class="keywordflow">if</span> (shape.num_dimensions() == 0)</div>
+<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;    {</div>
+<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;        shape.set_num_dimensions(1);</div>
+<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;    }</div>
+<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    <span class="keywordflow">return</span> shape;</div>
+<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;}</div>
+<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160; </div>
+<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;<span class="comment">// Utility function used to build a TensorInfo object, that can be used to initialise</span></div>
+<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;<span class="comment">// ARM Compute Tensor and CLTensor allocators.</span></div>
+<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;<span class="comment">// Note: this utility ignores the value of armnn::TensorInfo.IsConstant(). ACL tensors</span></div>
+<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;<span class="comment">// default to constant but Arm NN ones default to non constant. In the cases where</span></div>
+<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;<span class="comment">// we expect ACL to treat a tensor as constant that value must be set after this</span></div>
+<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;<span class="comment">// utility has been called.</span></div>
+<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; tensorInfo)</div>
+<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;{</div>
+<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;    <span class="keywordtype">bool</span> multiScales = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#af672d1c9e2a120a18926cb645981fbb7">HasMultipleQuantizationScales</a>();</div>
+<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;    <span class="keyword">const</span> arm_compute::TensorShape aclTensorShape = BuildArmComputeTensorShape(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div>
+<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> aclDataType       = GetArmComputeDataType(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#aea909c7327109228ef618d459015def3">GetDataType</a>(), multiScales);</div>
+<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160; </div>
+<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;    <span class="keyword">const</span> arm_compute::QuantizationInfo aclQuantizationInfo = multiScales ?</div>
+<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;        arm_compute::QuantizationInfo(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8bc11f1fa23ef42532f9fdd04d355270">GetQuantizationScales</a>()) :</div>
+<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;        arm_compute::QuantizationInfo(tensorInfo.GetQuantizationScale(), tensorInfo.GetQuantizationOffset());</div>
+<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160; </div>
+<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;    <span class="keywordflow">return</span> arm_compute::TensorInfo(aclTensorShape, 1, aclDataType, aclQuantizationInfo);</div>
+<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;}</div>
+<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160; </div>
+<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; tensorInfo,</div>
+<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;                                                  <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div>
+<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;{</div>
+<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;    arm_compute::TensorInfo aclTensorInfo = BuildArmComputeTensorInfo(tensorInfo);</div>
+<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    aclTensorInfo.set_data_layout(ConvertDataLayout(dataLayout));</div>
+<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160; </div>
+<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;    <span class="keywordflow">return</span> aclTensorInfo;</div>
+<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;}</div>
+<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160; </div>
+<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; tensorInfo, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimensions)</div>
+<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;{</div>
+<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    <span class="keywordtype">bool</span> multiScales = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#af672d1c9e2a120a18926cb645981fbb7">HasMultipleQuantizationScales</a>();</div>
+<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;    <span class="keyword">const</span> arm_compute::TensorShape aclTensorShape = BuildArmComputeTensorShape(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), dimensions);</div>
+<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a> aclDataType       = GetArmComputeDataType(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#aea909c7327109228ef618d459015def3">GetDataType</a>(), multiScales);</div>
+<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160; </div>
+<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;    <span class="keyword">const</span> arm_compute::QuantizationInfo aclQuantizationInfo = multiScales ?</div>
+<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;              arm_compute::QuantizationInfo(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8bc11f1fa23ef42532f9fdd04d355270">GetQuantizationScales</a>()) :</div>
+<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;              arm_compute::QuantizationInfo(tensorInfo.GetQuantizationScale(), tensorInfo.GetQuantizationOffset());</div>
+<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160; </div>
+<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;    <span class="keywordflow">return</span> arm_compute::TensorInfo(aclTensorShape, 1, aclDataType, aclQuantizationInfo);</div>
+<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;}</div>
+<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;arm_compute::TensorInfo BuildArmComputeTensorInfo(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; tensorInfo,</div>
+<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;                                                  <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimensions)</div>
+<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;{</div>
+<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;    arm_compute::TensorInfo aclTensorInfo = BuildArmComputeTensorInfo(tensorInfo, dimensions);</div>
+<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    aclTensorInfo.set_data_layout(ConvertDataLayout(dataLayout));</div>
+<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160; </div>
+<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;    <span class="keywordflow">return</span> aclTensorInfo;</div>
+<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;}</div>
+<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160; </div>
 <div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160; </div>
-<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160; </div>
-<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;<a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a> ConvertDataLayout(<a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div>
-<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;{</div>
-<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;    <span class="keywordflow">switch</span>(dataLayout)</div>
-<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;    {</div>
-<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NHWC;</div>
-<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160; </div>
-<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NCHW;</div>
-<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160; </div>
-<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef">armnn::DataLayout::NDHWC</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NDHWC;</div>
-<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160; </div>
-<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a884e0167ebf9bbe6cfd6ca5ab97ab015">armnn::DataLayout::NCDHW</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NCDHW;</div>
-<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160; </div>
-<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;        <span class="keywordflow">default</span>: <span class="keywordflow">throw</span> InvalidArgumentException(<span class="stringliteral">&quot;Unknown armnn::DataLayout: [&quot;</span> +</div>
-<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;                                                std::to_string(<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(dataLayout)) + <span class="stringliteral">&quot;]&quot;</span>);</div>
-<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;    }</div>
-<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;}</div>
-<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160; </div>
-<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(<span class="keyword">const</span> <a class="code" href="namespacearmnn_deserializer.html#a7e75f47f676327bce37149932aa4a011">Pooling2dDescriptor</a>&amp; descriptor,</div>
-<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;                                                              <span class="keywordtype">bool</span> fpMixedPrecision)</div>
-<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;{</div>
-<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    <span class="comment">// Resolve ARM Compute layer parameters.</span></div>
-<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;    <span class="keyword">const</span> arm_compute::PoolingType poolingType = <a class="code" href="namespacearmnn.html#ad256fcf8c7f4d5a240fa47f0b56d50af">ConvertPoolingAlgorithmToAclPoolingType</a>(descriptor.m_PoolType);</div>
-<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160; </div>
-<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a> dataLayout = ConvertDataLayout(descriptor.m_DataLayout);</div>
-<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160; </div>
-<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    <span class="keywordtype">bool</span> isGlobalPooling = (descriptor.m_StrideX==0 &amp;&amp; descriptor.m_StrideY==0);</div>
-<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;    <span class="comment">//use specific constructor if global pooling</span></div>
-<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;    <span class="keywordflow">if</span>(isGlobalPooling)</div>
-<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;    {</div>
-<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;        <span class="keywordflow">return</span> arm_compute::PoolingLayerInfo(poolingType, dataLayout);</div>
-<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;    }</div>
-<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160; </div>
-<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;    <span class="keyword">const</span> arm_compute::DimensionRoundingType rounding = <a class="code" href="namespacearmnn.html#a8f3bfacadfd6d2146d6ccd299dabc7aa">ConvertOutputShapeRoundingToAclDimensionRoundingType</a>(</div>
-<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;                                                                                    descriptor.m_OutputShapeRounding);</div>
-<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    <span class="keyword">const</span> arm_compute::PadStrideInfo padStrideInfo(descriptor.m_StrideX,</div>
-<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;                                                   descriptor.m_StrideY,</div>
-<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;                                                   descriptor.m_PadLeft,</div>
-<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;                                                   descriptor.m_PadRight,</div>
-<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;                                                   descriptor.m_PadTop,</div>
-<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;                                                   descriptor.m_PadBottom,</div>
-<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;                                                   rounding);</div>
-<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160; </div>
-<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> excludePadding = (descriptor.m_PaddingMethod == <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">PaddingMethod::Exclude</a>);</div>
-<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160; </div>
-<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;    <span class="keyword">const</span> arm_compute::Size2D poolSize(descriptor.m_PoolWidth, descriptor.m_PoolHeight);</div>
-<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160; </div>
-<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;    <span class="keywordflow">return</span> arm_compute::PoolingLayerInfo(poolingType, poolSize, dataLayout, padStrideInfo, excludePadding,</div>
-<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;                                         fpMixedPrecision);</div>
-<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;}</div>
-<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160; </div>
-<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;arm_compute::Pooling3dLayerInfo BuildArmComputePooling3dLayerInfo(<span class="keyword">const</span> <a class="code" href="namespacearmnn_deserializer.html#a6713b8a83104db317823b5367b195d2e">Pooling3dDescriptor</a>&amp; descriptor,</div>
-<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;                                                                  <span class="keywordtype">bool</span> fpMixedPrecision)</div>
-<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;{</div>
-<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    <span class="keyword">const</span> arm_compute::PoolingType poolingType = <a class="code" href="namespacearmnn.html#ad256fcf8c7f4d5a240fa47f0b56d50af">ConvertPoolingAlgorithmToAclPoolingType</a>(descriptor.m_PoolType);</div>
-<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160; </div>
-<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    <span class="keywordtype">bool</span> isGlobalPooling = (descriptor.m_StrideX==0 &amp;&amp; descriptor.m_StrideY==0 &amp;&amp; descriptor.m_StrideZ==0);</div>
-<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    <span class="comment">//use specific constructor if global pooling</span></div>
-<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    <span class="keywordflow">if</span>(isGlobalPooling)</div>
-<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;    {</div>
-<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;        <span class="keywordflow">return</span> arm_compute::Pooling3dLayerInfo(poolingType);</div>
-<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;    }</div>
-<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160; </div>
-<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;    <span class="keyword">const</span> arm_compute::Size3D poolSize(descriptor.m_PoolWidth, descriptor.m_PoolHeight, descriptor.m_PoolDepth);</div>
-<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160; </div>
-<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;    <span class="keyword">const</span> arm_compute::Size3D stride(descriptor.m_StrideX,</div>
-<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;                        descriptor.m_StrideY,</div>
-<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;                        descriptor.m_StrideZ);</div>
-<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160; </div>
-<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    <span class="keyword">const</span> arm_compute::Padding3D padding(descriptor.m_PadLeft,</div>
-<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;                            descriptor.m_PadRight,</div>
-<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;                            descriptor.m_PadTop,</div>
-<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;                            descriptor.m_PadBottom,</div>
-<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;                            descriptor.m_PadFront,</div>
-<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;                            descriptor.m_PadBack);</div>
-<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160; </div>
-<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> excludePadding = (descriptor.m_PaddingMethod == <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">PaddingMethod::Exclude</a>);</div>
-<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160; </div>
-<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    <span class="keyword">const</span> arm_compute::DimensionRoundingType rounding = <a class="code" href="namespacearmnn.html#a8f3bfacadfd6d2146d6ccd299dabc7aa">ConvertOutputShapeRoundingToAclDimensionRoundingType</a>(</div>
-<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;            descriptor.m_OutputShapeRounding);</div>
-<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160; </div>
-<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    <span class="keywordflow">return</span> arm_compute::Pooling3dLayerInfo(poolingType,</div>
-<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;                                           poolSize,</div>
-<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;                                           stride,</div>
-<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;                                           padding,</div>
-<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;                                           excludePadding,</div>
-<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;                                           fpMixedPrecision,</div>
-<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;                                           rounding);</div>
-<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;}</div>
-<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160; </div>
-<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;arm_compute::NormalizationLayerInfo BuildArmComputeNormalizationLayerInfo(<span class="keyword">const</span> NormalizationDescriptor&amp; descriptor)</div>
-<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;{</div>
-<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;    <span class="keyword">const</span> arm_compute::NormType normType =</div>
-<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;        <a class="code" href="namespacearmnn.html#aa5baabb8e3a4aa6cbdcab419d743e747">ConvertNormalizationAlgorithmChannelToAclNormType</a>(descriptor.m_NormChannelType);</div>
-<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;    <span class="keywordflow">return</span> arm_compute::NormalizationLayerInfo(normType,</div>
-<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;                                               descriptor.m_NormSize,</div>
-<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;                                               descriptor.m_Alpha,</div>
-<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;                                               descriptor.m_Beta,</div>
-<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;                                               descriptor.m_K,</div>
-<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;                                               <span class="keyword">false</span>);</div>
-<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;}</div>
-<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160; </div>
-<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;arm_compute::PermutationVector BuildArmComputePermutationVector(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a>&amp; perm)</div>
-<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;{</div>
-<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;    arm_compute::PermutationVector aclPerm;</div>
-<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160; </div>
-<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> start = 0;</div>
-<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;    <span class="keywordflow">while</span> ((start &lt; perm.<a class="code" href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>()) &amp;&amp; (start == perm[start]))</div>
-<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;    {</div>
-<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;        ++start;</div>
-<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;    }</div>
-<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160; </div>
-<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = start; i &lt; perm.<a class="code" href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(); ++i)</div>
-<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;    {</div>
-<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;        aclPerm.set(i - start, perm[i] - start);</div>
-<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;    }</div>
-<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;    <span class="keywordflow">return</span> aclPerm;</div>
-<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;}</div>
-<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160; </div>
-<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;arm_compute::PermutationVector BuildArmComputeTransposeVector(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a>&amp; perm)</div>
-<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;{</div>
-<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;    <span class="comment">// As ArmNN indexes are left to right and ACL indexes are right to left,</span></div>
-<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;    <span class="comment">// the permutation vector has to be reversed and then translated into ACL axis.</span></div>
-<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;    <span class="comment">// i.e. {1, 0, 2, 3} --&gt; {3, 2, 0, 1} --&gt; {0, 1, 3, 2}</span></div>
-<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160; </div>
-<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;    <span class="comment">// Below an example of how the ArmNN and ACL index format work:</span></div>
-<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;    <span class="comment">// ArmNN Format:</span></div>
-<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;    <span class="comment">// Input Shape        {1, 10, 20, 30}</span></div>
-<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;    <span class="comment">// Permutation Vector {1,  0,  2,  3}</span></div>
-<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;    <span class="comment">// Output Shape       {10, 1, 20, 30}</span></div>
-<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;    <span class="comment">// dim &quot;1&quot; of input goes into index 0 of the output ([ 10, X, X, X])</span></div>
-<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;    <span class="comment">// dim &quot;0&quot; of input goes into index 1 of the output ([ 10, 1, X, X ])</span></div>
-<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;    <span class="comment">// dim &quot;2&quot; of input goes into index 2 of the output ([ 10, 1, 20, X ])</span></div>
-<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;    <span class="comment">// dim &quot;3&quot; of input goes into index 3 of the output ([ 10, 1, 20, 30 ])</span></div>
-<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;    <span class="comment">// ACL Format:</span></div>
-<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;    <span class="comment">// Input Shape        {30, 20, 10, 1}</span></div>
-<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;    <span class="comment">// Permutation Vector {0,  1,  3,  2}</span></div>
-<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;    <span class="comment">// Output Shape       {30, 20, 1, 10}</span></div>
-<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;    <span class="comment">// dim &quot;0&quot; of input goes into index 0 of the output ([ 30,  X, X, X])</span></div>
-<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;    <span class="comment">// dim &quot;1&quot; of input goes into index 1 of the output ([ 30, 20, X, X ])</span></div>
-<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;    <span class="comment">// dim &quot;3&quot; of input goes into index 2 of the output ([ 30, 20, 1, X ])</span></div>
-<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;    <span class="comment">// dim &quot;2&quot; of input goes into index 3 of the output ([ 30, 20, 1, 10 ])</span></div>
-<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160; </div>
-<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;    arm_compute::PermutationVector aclPerm;</div>
-<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;    <span class="keyword">auto</span> rank = perm.<a class="code" href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>();</div>
-<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160; </div>
-<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;    <span class="comment">// Reverse the order. i.e. {1, 0, 2, 3} --&gt; {3, 2, 0, 1}</span></div>
-<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;    std::vector&lt;unsigned int&gt; reversedPerm;</div>
-<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;    reversedPerm.reserve(rank);</div>
-<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = rank; i &gt; 0; --i)</div>
-<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    {</div>
-<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;        reversedPerm.push_back(perm[i-1]);</div>
-<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;    }</div>
-<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160; </div>
-<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;    <span class="comment">// Translate from Arm NN axis to ACL axis. i.e. {3, 2, 0, 1} --&gt; {0, 1, 3, 2}</span></div>
-<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; rank; ++i)</div>
-<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;    {</div>
-<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;        <span class="keyword">auto</span> aclAxis = rank - 1 - reversedPerm[i];</div>
-<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;        aclPerm.set(i, aclAxis);</div>
-<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;    }</div>
-<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;    <span class="keywordflow">return</span> aclPerm;</div>
-<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;}</div>
-<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160; </div>
-<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;arm_compute::Size2D BuildArmComputeSize2D(<span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height)</div>
-<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;{</div>
-<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;    <span class="keywordflow">return</span> arm_compute::Size2D(width, height);</div>
-<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;}</div>
-<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160; </div>
-<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;arm_compute::PixelValue GetPixelValue(<span class="keyword">const</span> arm_compute::ITensorInfo* tensorInfo, <span class="keywordtype">float</span> value)</div>
-<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;{</div>
-<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;    <span class="keywordflow">switch</span> (tensorInfo-&gt;data_type())</div>
-<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;    {</div>
-<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::F16:</div>
-<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;        {</div>
-<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;            arm_compute::PixelValue pixelValue = arm_compute::PixelValue(<span class="keyword">static_cast&lt;</span><a class="code" href="namespacearmnn.html#a0b49aa352b84d572942185ce72cef751">Half</a><span class="keyword">&gt;</span>(value));</div>
-<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;            <span class="keywordflow">if</span> (isinf(pixelValue.get&lt;<a class="code" href="namespacearmnn.html#a0b49aa352b84d572942185ce72cef751">Half</a>&gt;())) {</div>
-<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;                <span class="keywordflow">throw</span> InvalidArgumentException(<span class="stringliteral">&quot;Under/Overflow converting float value [&quot;</span> + std::to_string(value) +</div>
-<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;                    <span class="stringliteral">&quot;] to fp16: [&quot;</span> + std::to_string(pixelValue.get&lt;<a class="code" href="namespacearmnn.html#a0b49aa352b84d572942185ce72cef751">Half</a>&gt;()) + <span class="stringliteral">&quot;]&quot;</span>);</div>
-<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;            }</div>
-<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;            <span class="keywordflow">return</span> pixelValue;</div>
-<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;        }</div>
-<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::F32:</div>
-<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;            <span class="keywordflow">return</span> arm_compute::PixelValue(value);</div>
-<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::QASYMM8:</div>
-<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;            <span class="keywordflow">return</span> arm_compute::PixelValue(<span class="keyword">static_cast&lt;</span>uint8_t<span class="keyword">&gt;</span>(value));</div>
-<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::QSYMM16:</div>
-<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;            <span class="keywordflow">return</span> arm_compute::PixelValue(<span class="keyword">static_cast&lt;</span>int16_t<span class="keyword">&gt;</span>(value));</div>
-<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::QSYMM8:</div>
-<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::QASYMM8_SIGNED:</div>
-<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::QSYMM8_PER_CHANNEL:</div>
-<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;            <span class="keywordflow">return</span> arm_compute::PixelValue(<span class="keyword">static_cast&lt;</span>int8_t<span class="keyword">&gt;</span>(value));</div>
-<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::S32:</div>
-<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;            <span class="keywordflow">return</span> arm_compute::PixelValue(<span class="keyword">static_cast&lt;</span>int32_t<span class="keyword">&gt;</span>(value));</div>
-<div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;        <span class="keywordflow">default</span>:</div>
-<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;            <span class="keywordflow">throw</span> InvalidArgumentException(<span class="stringliteral">&quot;Unsupported DataType: [&quot;</span> +</div>
-<div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;                                           std::to_string(<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(tensorInfo-&gt;data_type())) + <span class="stringliteral">&quot;]&quot;</span>);</div>
-<div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;    }</div>
-<div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;}</div>
-<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160; </div>
-<div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> ComputeDepthwiseConv2dDepthMultiplier(<a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout,</div>
-<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;                                                   <span class="keyword">const</span> arm_compute::TensorShape&amp; weightsShape,</div>
-<div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;                                                   <span class="keyword">const</span> arm_compute::TensorShape&amp; inputShape)</div>
-<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;{</div>
-<div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depthMultiplier;</div>
-<div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;    <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div>
-<div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;    {</div>
-<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;        depthMultiplier = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(weightsShape[0]) / <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(inputShape[0]);</div>
-<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;    }</div>
-<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;    <span class="keywordflow">else</span> <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>)</div>
-<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;    {</div>
-<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;        depthMultiplier = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(weightsShape[2]) / <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(inputShape[2]);</div>
-<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;    }</div>
-<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;    <span class="keywordflow">else</span></div>
-<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;    {</div>
-<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;        <span class="keywordflow">throw</span> InvalidArgumentException(fmt::format(<span class="stringliteral">&quot;Unknown data layout for tensor conversion: {}&quot;</span>,</div>
-<div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;                                                   <a class="code" href="namespacearmnn.html#aeef70b7611ae71e97ab55c75ef72b210">GetDataLayoutName</a>(layout)));</div>
-<div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;    }</div>
-<div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;    <span class="keywordflow">return</span> depthMultiplier;</div>
-<div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;}</div>
-<div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160; </div>
-<div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;} <span class="comment">// namespace armcomputetensorutils</span></div>
-<div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;} <span class="comment">// namespace armnn</span></div>
+<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;<a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a> ConvertDataLayout(<a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div>
+<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;{</div>
+<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    <span class="keywordflow">switch</span>(dataLayout)</div>
+<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;    {</div>
+<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NHWC;</div>
+<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160; </div>
+<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NCHW;</div>
+<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160; </div>
+<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef">armnn::DataLayout::NDHWC</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NDHWC;</div>
+<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160; </div>
+<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a884e0167ebf9bbe6cfd6ca5ab97ab015">armnn::DataLayout::NCDHW</a> : <span class="keywordflow">return</span> arm_compute::DataLayout::NCDHW;</div>
+<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160; </div>
+<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;        <span class="keywordflow">default</span>: <span class="keywordflow">throw</span> InvalidArgumentException(<span class="stringliteral">&quot;Unknown armnn::DataLayout: [&quot;</span> +</div>
+<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;                                                std::to_string(<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(dataLayout)) + <span class="stringliteral">&quot;]&quot;</span>);</div>
+<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;    }</div>
+<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;}</div>
+<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160; </div>
+<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(<span class="keyword">const</span> <a class="code" href="namespacearmnn_deserializer.html#a7e75f47f676327bce37149932aa4a011">Pooling2dDescriptor</a>&amp; descriptor,</div>
+<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;                                                              <span class="keywordtype">bool</span> fpMixedPrecision)</div>
+<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;{</div>
+<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;    <span class="comment">// Resolve ARM Compute layer parameters.</span></div>
+<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    <span class="keyword">const</span> arm_compute::PoolingType poolingType = <a class="code" href="namespacearmnn.html#ad256fcf8c7f4d5a240fa47f0b56d50af">ConvertPoolingAlgorithmToAclPoolingType</a>(descriptor.m_PoolType);</div>
+<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160; </div>
+<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;    <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a> dataLayout = ConvertDataLayout(descriptor.m_DataLayout);</div>
+<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160; </div>
+<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;    <span class="keywordtype">bool</span> isGlobalPooling = (descriptor.m_StrideX==0 &amp;&amp; descriptor.m_StrideY==0);</div>
+<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    <span class="comment">//use specific constructor if global pooling</span></div>
+<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;    <span class="keywordflow">if</span>(isGlobalPooling)</div>
+<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;    {</div>
+<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;        <span class="keywordflow">return</span> arm_compute::PoolingLayerInfo(poolingType, dataLayout);</div>
+<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    }</div>
+<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160; </div>
+<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    <span class="keyword">const</span> arm_compute::DimensionRoundingType rounding = <a class="code" href="namespacearmnn.html#a8f3bfacadfd6d2146d6ccd299dabc7aa">ConvertOutputShapeRoundingToAclDimensionRoundingType</a>(</div>
+<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;                                                                                    descriptor.m_OutputShapeRounding);</div>
+<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    <span class="keyword">const</span> arm_compute::PadStrideInfo padStrideInfo(descriptor.m_StrideX,</div>
+<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;                                                   descriptor.m_StrideY,</div>
+<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;                                                   descriptor.m_PadLeft,</div>
+<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;                                                   descriptor.m_PadRight,</div>
+<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;                                                   descriptor.m_PadTop,</div>
+<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;                                                   descriptor.m_PadBottom,</div>
+<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;                                                   rounding);</div>
+<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160; </div>
+<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> excludePadding = (descriptor.m_PaddingMethod == <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">PaddingMethod::Exclude</a>);</div>
+<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160; </div>
+<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;    <span class="keyword">const</span> arm_compute::Size2D poolSize(descriptor.m_PoolWidth, descriptor.m_PoolHeight);</div>
+<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160; </div>
+<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    <span class="keywordflow">return</span> arm_compute::PoolingLayerInfo(poolingType, poolSize, dataLayout, padStrideInfo, excludePadding,</div>
+<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;                                         fpMixedPrecision);</div>
+<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;}</div>
+<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160; </div>
+<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;arm_compute::Pooling3dLayerInfo BuildArmComputePooling3dLayerInfo(<span class="keyword">const</span> <a class="code" href="namespacearmnn_deserializer.html#a6713b8a83104db317823b5367b195d2e">Pooling3dDescriptor</a>&amp; descriptor,</div>
+<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;                                                                  <span class="keywordtype">bool</span> fpMixedPrecision)</div>
+<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;{</div>
+<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    <span class="keyword">const</span> arm_compute::PoolingType poolingType = <a class="code" href="namespacearmnn.html#ad256fcf8c7f4d5a240fa47f0b56d50af">ConvertPoolingAlgorithmToAclPoolingType</a>(descriptor.m_PoolType);</div>
+<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160; </div>
+<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    <span class="keywordtype">bool</span> isGlobalPooling = (descriptor.m_StrideX==0 &amp;&amp; descriptor.m_StrideY==0 &amp;&amp; descriptor.m_StrideZ==0);</div>
+<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    <span class="comment">//use specific constructor if global pooling</span></div>
+<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    <span class="keywordflow">if</span>(isGlobalPooling)</div>
+<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    {</div>
+<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;        <span class="keywordflow">return</span> arm_compute::Pooling3dLayerInfo(poolingType);</div>
+<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    }</div>
+<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160; </div>
+<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;    <span class="keyword">const</span> arm_compute::Size3D poolSize(descriptor.m_PoolWidth, descriptor.m_PoolHeight, descriptor.m_PoolDepth);</div>
+<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160; </div>
+<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    <span class="keyword">const</span> arm_compute::Size3D stride(descriptor.m_StrideX,</div>
+<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;                        descriptor.m_StrideY,</div>
+<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;                        descriptor.m_StrideZ);</div>
+<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160; </div>
+<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    <span class="keyword">const</span> arm_compute::Padding3D padding(descriptor.m_PadLeft,</div>
+<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;                            descriptor.m_PadRight,</div>
+<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;                            descriptor.m_PadTop,</div>
+<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;                            descriptor.m_PadBottom,</div>
+<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;                            descriptor.m_PadFront,</div>
+<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;                            descriptor.m_PadBack);</div>
+<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160; </div>
+<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">bool</span> excludePadding = (descriptor.m_PaddingMethod == <a class="code" href="namespacearmnn.html#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">PaddingMethod::Exclude</a>);</div>
+<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160; </div>
+<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    <span class="keyword">const</span> arm_compute::DimensionRoundingType rounding = <a class="code" href="namespacearmnn.html#a8f3bfacadfd6d2146d6ccd299dabc7aa">ConvertOutputShapeRoundingToAclDimensionRoundingType</a>(</div>
+<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;            descriptor.m_OutputShapeRounding);</div>
+<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160; </div>
+<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;    <span class="keywordflow">return</span> arm_compute::Pooling3dLayerInfo(poolingType,</div>
+<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;                                           poolSize,</div>
+<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;                                           stride,</div>
+<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;                                           padding,</div>
+<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;                                           excludePadding,</div>
+<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;                                           fpMixedPrecision,</div>
+<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;                                           rounding);</div>
+<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;}</div>
+<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160; </div>
+<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;arm_compute::NormalizationLayerInfo BuildArmComputeNormalizationLayerInfo(<span class="keyword">const</span> NormalizationDescriptor&amp; descriptor)</div>
+<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;{</div>
+<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    <span class="keyword">const</span> arm_compute::NormType normType =</div>
+<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;        <a class="code" href="namespacearmnn.html#aa5baabb8e3a4aa6cbdcab419d743e747">ConvertNormalizationAlgorithmChannelToAclNormType</a>(descriptor.m_NormChannelType);</div>
+<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;    <span class="keywordflow">return</span> arm_compute::NormalizationLayerInfo(normType,</div>
+<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;                                               descriptor.m_NormSize,</div>
+<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;                                               descriptor.m_Alpha,</div>
+<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;                                               descriptor.m_Beta,</div>
+<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;                                               descriptor.m_K,</div>
+<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;                                               <span class="keyword">false</span>);</div>
+<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;}</div>
+<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160; </div>
+<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;arm_compute::PermutationVector BuildArmComputePermutationVector(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a>&amp; perm)</div>
+<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;{</div>
+<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;    arm_compute::PermutationVector aclPerm;</div>
+<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160; </div>
+<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> start = 0;</div>
+<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;    <span class="keywordflow">while</span> ((start &lt; perm.<a class="code" href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>()) &amp;&amp; (start == perm[start]))</div>
+<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;    {</div>
+<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;        ++start;</div>
+<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;    }</div>
+<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160; </div>
+<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = start; i &lt; perm.<a class="code" href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(); ++i)</div>
+<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;    {</div>
+<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;        aclPerm.set(i - start, perm[i] - start);</div>
+<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;    }</div>
+<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;    <span class="keywordflow">return</span> aclPerm;</div>
+<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;}</div>
+<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160; </div>
+<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;arm_compute::PermutationVector BuildArmComputeTransposeVector(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a>&amp; perm)</div>
+<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;{</div>
+<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;    <span class="comment">// As ArmNN indexes are left to right and ACL indexes are right to left,</span></div>
+<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;    <span class="comment">// the permutation vector has to be reversed and then translated into ACL axis.</span></div>
+<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;    <span class="comment">// i.e. {1, 0, 2, 3} --&gt; {3, 2, 0, 1} --&gt; {0, 1, 3, 2}</span></div>
+<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160; </div>
+<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;    <span class="comment">// Below an example of how the ArmNN and ACL index format work:</span></div>
+<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;    <span class="comment">// ArmNN Format:</span></div>
+<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;    <span class="comment">// Input Shape        {1, 10, 20, 30}</span></div>
+<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;    <span class="comment">// Permutation Vector {1,  0,  2,  3}</span></div>
+<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;    <span class="comment">// Output Shape       {10, 1, 20, 30}</span></div>
+<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;    <span class="comment">// dim &quot;1&quot; of input goes into index 0 of the output ([ 10, X, X, X])</span></div>
+<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;    <span class="comment">// dim &quot;0&quot; of input goes into index 1 of the output ([ 10, 1, X, X ])</span></div>
+<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;    <span class="comment">// dim &quot;2&quot; of input goes into index 2 of the output ([ 10, 1, 20, X ])</span></div>
+<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;    <span class="comment">// dim &quot;3&quot; of input goes into index 3 of the output ([ 10, 1, 20, 30 ])</span></div>
+<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;    <span class="comment">// ACL Format:</span></div>
+<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;    <span class="comment">// Input Shape        {30, 20, 10, 1}</span></div>
+<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;    <span class="comment">// Permutation Vector {0,  1,  3,  2}</span></div>
+<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;    <span class="comment">// Output Shape       {30, 20, 1, 10}</span></div>
+<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;    <span class="comment">// dim &quot;0&quot; of input goes into index 0 of the output ([ 30,  X, X, X])</span></div>
+<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;    <span class="comment">// dim &quot;1&quot; of input goes into index 1 of the output ([ 30, 20, X, X ])</span></div>
+<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;    <span class="comment">// dim &quot;3&quot; of input goes into index 2 of the output ([ 30, 20, 1, X ])</span></div>
+<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;    <span class="comment">// dim &quot;2&quot; of input goes into index 3 of the output ([ 30, 20, 1, 10 ])</span></div>
+<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160; </div>
+<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;    arm_compute::PermutationVector aclPerm;</div>
+<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;    <span class="keyword">auto</span> rank = perm.<a class="code" href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>();</div>
+<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160; </div>
+<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;    <span class="comment">// Reverse the order. i.e. {1, 0, 2, 3} --&gt; {3, 2, 0, 1}</span></div>
+<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;    std::vector&lt;unsigned int&gt; reversedPerm;</div>
+<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;    reversedPerm.reserve(rank);</div>
+<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = rank; i &gt; 0; --i)</div>
+<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    {</div>
+<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;        reversedPerm.push_back(perm[i-1]);</div>
+<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;    }</div>
+<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160; </div>
+<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;    <span class="comment">// Translate from Arm NN axis to ACL axis. i.e. {3, 2, 0, 1} --&gt; {0, 1, 3, 2}</span></div>
+<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; rank; ++i)</div>
+<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;    {</div>
+<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;        <span class="keyword">auto</span> aclAxis = rank - 1 - reversedPerm[i];</div>
+<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;        aclPerm.set(i, aclAxis);</div>
+<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;    }</div>
+<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;    <span class="keywordflow">return</span> aclPerm;</div>
+<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;}</div>
+<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160; </div>
+<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;arm_compute::Size2D BuildArmComputeSize2D(<span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height)</div>
+<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;{</div>
+<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;    <span class="keywordflow">return</span> arm_compute::Size2D(width, height);</div>
+<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;}</div>
+<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160; </div>
+<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;arm_compute::PixelValue GetPixelValue(<span class="keyword">const</span> arm_compute::ITensorInfo* tensorInfo, <span class="keywordtype">float</span> value)</div>
+<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;{</div>
+<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;    <span class="keywordflow">switch</span> (tensorInfo-&gt;data_type())</div>
+<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;    {</div>
+<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::F16:</div>
+<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;        {</div>
+<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;            arm_compute::PixelValue pixelValue = arm_compute::PixelValue(<span class="keyword">static_cast&lt;</span><a class="code" href="namespacearmnn.html#a0b49aa352b84d572942185ce72cef751">Half</a><span class="keyword">&gt;</span>(value));</div>
+<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;            <span class="keywordflow">if</span> (isinf(pixelValue.get&lt;<a class="code" href="namespacearmnn.html#a0b49aa352b84d572942185ce72cef751">Half</a>&gt;())) {</div>
+<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;                <span class="keywordflow">throw</span> InvalidArgumentException(<span class="stringliteral">&quot;Under/Overflow converting float value [&quot;</span> + std::to_string(value) +</div>
+<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;                    <span class="stringliteral">&quot;] to fp16: [&quot;</span> + std::to_string(pixelValue.get&lt;<a class="code" href="namespacearmnn.html#a0b49aa352b84d572942185ce72cef751">Half</a>&gt;()) + <span class="stringliteral">&quot;]&quot;</span>);</div>
+<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;            }</div>
+<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;            <span class="keywordflow">return</span> pixelValue;</div>
+<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;        }</div>
+<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::F32:</div>
+<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;            <span class="keywordflow">return</span> arm_compute::PixelValue(value);</div>
+<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::QASYMM8:</div>
+<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;            <span class="keywordflow">return</span> arm_compute::PixelValue(<span class="keyword">static_cast&lt;</span>uint8_t<span class="keyword">&gt;</span>(value));</div>
+<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::QSYMM16:</div>
+<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;            <span class="keywordflow">return</span> arm_compute::PixelValue(<span class="keyword">static_cast&lt;</span>int16_t<span class="keyword">&gt;</span>(value));</div>
+<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::QSYMM8:</div>
+<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::QASYMM8_SIGNED:</div>
+<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::QSYMM8_PER_CHANNEL:</div>
+<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;            <span class="keywordflow">return</span> arm_compute::PixelValue(<span class="keyword">static_cast&lt;</span>int8_t<span class="keyword">&gt;</span>(value));</div>
+<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;        <span class="keywordflow">case</span> arm_compute::DataType::S32:</div>
+<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;            <span class="keywordflow">return</span> arm_compute::PixelValue(<span class="keyword">static_cast&lt;</span>int32_t<span class="keyword">&gt;</span>(value));</div>
+<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;        <span class="keywordflow">default</span>:</div>
+<div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;            <span class="keywordflow">throw</span> InvalidArgumentException(<span class="stringliteral">&quot;Unsupported DataType: [&quot;</span> +</div>
+<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;                                           std::to_string(<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(tensorInfo-&gt;data_type())) + <span class="stringliteral">&quot;]&quot;</span>);</div>
+<div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;    }</div>
+<div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;}</div>
+<div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160; </div>
+<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> ComputeDepthwiseConv2dDepthMultiplier(<a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout,</div>
+<div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;                                                   <span class="keyword">const</span> arm_compute::TensorShape&amp; weightsShape,</div>
+<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;                                                   <span class="keyword">const</span> arm_compute::TensorShape&amp; inputShape)</div>
+<div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;{</div>
+<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depthMultiplier;</div>
+<div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;    <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div>
+<div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;    {</div>
+<div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;        depthMultiplier = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(weightsShape[0]) / <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(inputShape[0]);</div>
+<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;    }</div>
+<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;    <span class="keywordflow">else</span> <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>)</div>
+<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;    {</div>
+<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;        depthMultiplier = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(weightsShape[2]) / <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span>(inputShape[2]);</div>
+<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;    }</div>
+<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;    <span class="keywordflow">else</span></div>
+<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;    {</div>
+<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;        <span class="keywordflow">throw</span> InvalidArgumentException(fmt::format(<span class="stringliteral">&quot;Unknown data layout for tensor conversion: {}&quot;</span>,</div>
+<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;                                                   <a class="code" href="namespacearmnn.html#aeef70b7611ae71e97ab55c75ef72b210">GetDataLayoutName</a>(layout)));</div>
+<div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;    }</div>
+<div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;    <span class="keywordflow">return</span> depthMultiplier;</div>
+<div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;}</div>
+<div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160; </div>
+<div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;arm_compute::ScatterInfo BuildArmComputeScatterInfo(<span class="keyword">const</span> ScatterNdDescriptor&amp; descriptor)</div>
+<div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;{</div>
+<div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;    arm_compute::ScatterFunction scatterFunction;</div>
+<div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;    <span class="keywordflow">switch</span>(descriptor.m_Function)</div>
+<div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;    {</div>
+<div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#a75ca90884e15396a70b0cb722a877b4aa06933067aafd48425d67bcb01bba5cb6">ScatterNdFunction::Update</a>:</div>
+<div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;            scatterFunction = arm_compute::ScatterFunction::Update;</div>
+<div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;            <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#a75ca90884e15396a70b0cb722a877b4aaec211f7c20af43e742bf2570c3cb84f9">ScatterNdFunction::Add</a>:</div>
+<div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;            scatterFunction = arm_compute::ScatterFunction::Add;</div>
+<div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;            <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#a75ca90884e15396a70b0cb722a877b4aae80155eceb940c89e2de63ad05868db2">ScatterNdFunction::Sub</a>:</div>
+<div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;            scatterFunction = arm_compute::ScatterFunction::Sub;</div>
+<div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;            <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#a75ca90884e15396a70b0cb722a877b4aa6a061313d22e51e0f25b7cd4dc065233">ScatterNdFunction::Max</a>:</div>
+<div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;            scatterFunction = arm_compute::ScatterFunction::Max;</div>
+<div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;            <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;        <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.html#a75ca90884e15396a70b0cb722a877b4aa78d811e98514cd165dda532286610fd2">ScatterNdFunction::Min</a>:</div>
+<div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;            scatterFunction = arm_compute::ScatterFunction::Min;</div>
+<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;            <span class="keywordflow">break</span>;</div>
+<div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;        <span class="keywordflow">default</span>: <span class="keywordflow">throw</span> InvalidArgumentException(<span class="stringliteral">&quot;Unknown ArmNN::ScatterNd Function: [&quot;</span> +</div>
+<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;                                                std::to_string(<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(descriptor.m_Function)) + <span class="stringliteral">&quot;]&quot;</span>);</div>
+<div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;    }</div>
+<div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160; </div>
+<div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;    <span class="keywordflow">return</span> arm_compute::ScatterInfo(scatterFunction, !descriptor.m_InputEnabled);</div>
+<div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;}</div>
+<div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;} <span class="comment">// namespace armcomputetensorutils</span></div>
+<div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;} <span class="comment">// namespace armnn</span></div>
 </div><!-- fragment --></div><!-- contents -->
 </div><!-- doc-content -->
 <div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a></div><div class="ttdeci">@ Boolean</div></div>
+<div class="ttc" id="anamespacearmnn_html_a75ca90884e15396a70b0cb722a877b4aa78d811e98514cd165dda532286610fd2"><div class="ttname"><a href="namespacearmnn.html#a75ca90884e15396a70b0cb722a877b4aa78d811e98514cd165dda532286610fd2">armnn::ScatterNdFunction::Min</a></div><div class="ttdeci">@ Min</div></div>
 <div class="ttc" id="anamespacearmnn_html_ad1d5cce2d9e9a5d61c243e5c989112e0a884e0167ebf9bbe6cfd6ca5ab97ab015"><div class="ttname"><a href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0a884e0167ebf9bbe6cfd6ca5ab97ab015">armnn::DataLayout::NCDHW</a></div><div class="ttdeci">@ NCDHW</div></div>
 <div class="ttc" id="anamespacearmnn_html_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00062">Types.hpp:62</a></div></div>
 <div class="ttc" id="a_descriptors_8hpp_html"><div class="ttname"><a href="_descriptors_8hpp.html">Descriptors.hpp</a></div></div>
 <div class="ttc" id="anamespacearmnn_html_a8f3bfacadfd6d2146d6ccd299dabc7aa"><div class="ttname"><a href="namespacearmnn.html#a8f3bfacadfd6d2146d6ccd299dabc7aa">armnn::ConvertOutputShapeRoundingToAclDimensionRoundingType</a></div><div class="ttdeci">arm_compute::DimensionRoundingType ConvertOutputShapeRoundingToAclDimensionRoundingType(OutputShapeRounding rounding)</div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_utils_8hpp_source.html#l00168">ArmComputeUtils.hpp:168</a></div></div>
 <div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a8bc11f1fa23ef42532f9fdd04d355270"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a8bc11f1fa23ef42532f9fdd04d355270">armnn::TensorInfo::GetQuantizationScales</a></div><div class="ttdeci">std::vector&lt; float &gt; GetQuantizationScales() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00451">Tensor.cpp:451</a></div></div>
+<div class="ttc" id="anamespacearmnn_html_a75ca90884e15396a70b0cb722a877b4aae80155eceb940c89e2de63ad05868db2"><div class="ttname"><a href="namespacearmnn.html#a75ca90884e15396a70b0cb722a877b4aae80155eceb940c89e2de63ad05868db2">armnn::ScatterNdFunction::Sub</a></div><div class="ttdeci">@ Sub</div></div>
 <div class="ttc" id="anamespacearmnn_html_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.html#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div><div class="ttdeci">@ NHWC</div></div>
 <div class="ttc" id="aclassarmnn_1_1_tensor_info_html"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00152">Tensor.hpp:152</a></div></div>
 <div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div><div class="ttdeci">@ Float32</div></div>
@@ -575,7 +602,6 @@
 <div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div><div class="ttdeci">@ QAsymmU8</div></div>
 <div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a></div><div class="ttdeci">@ QSymmS8</div></div>
 <div class="ttc" id="anamespacearmnn_html_a0b49aa352b84d572942185ce72cef751"><div class="ttname"><a href="namespacearmnn.html#a0b49aa352b84d572942185ce72cef751">armnn::Half</a></div><div class="ttdeci">half_float::half Half</div><div class="ttdef"><b>Definition:</b> <a href="_half_8hpp_source.html#l00022">Half.hpp:22</a></div></div>
-<div class="ttc" id="a_assert_8hpp_html_a91c4dfde57907d7698c7531785690a7f"><div class="ttname"><a href="_assert_8hpp.html#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a></div><div class="ttdeci">#define ARMNN_ASSERT_MSG(COND, MSG)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.html#l00015">Assert.hpp:15</a></div></div>
 <div class="ttc" id="anamespacearmnn_html_ac6e86c1def7f674d3c4cb7f577874aa6"><div class="ttname"><a href="namespacearmnn.html#ac6e86c1def7f674d3c4cb7f577874aa6">armnn::Coordinates</a></div><div class="ttdeci">std::array&lt; unsigned int, MaxNumOfTensorDimensions &gt; Coordinates</div><div class="ttdef"><b>Definition:</b> <a href="_internal_types_8hpp_source.html#l00015">InternalTypes.hpp:15</a></div></div>
 <div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div><div class="ttdeci">@ QSymmS16</div></div>
 <div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</a></div><div class="ttdeci">@ BFloat16</div></div>
@@ -588,12 +614,15 @@
 <div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00048">Types.hpp:48</a></div></div>
 <div class="ttc" id="a_arm_compute_utils_8hpp_html"><div class="ttname"><a href="_arm_compute_utils_8hpp.html">ArmComputeUtils.hpp</a></div></div>
 <div class="ttc" id="aclassarmnn_1_1_permutation_vector_html"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.html">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00314">Types.hpp:314</a></div></div>
+<div class="ttc" id="anamespacearmnn_html_a75ca90884e15396a70b0cb722a877b4aaec211f7c20af43e742bf2570c3cb84f9"><div class="ttname"><a href="namespacearmnn.html#a75ca90884e15396a70b0cb722a877b4aaec211f7c20af43e742bf2570c3cb84f9">armnn::ScatterNdFunction::Add</a></div><div class="ttdeci">@ Add</div></div>
 <div class="ttc" id="aclassarmnn_1_1_tensor_info_html_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00200">Tensor.hpp:200</a></div></div>
 <div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div><div class="ttdeci">@ Signed32</div></div>
 <div class="ttc" id="anamespacearmnn_html_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div><div class="ttdeci">@ QAsymmS8</div></div>
 <div class="ttc" id="aclassarmnn_1_1_permutation_vector_html_a490ec6b59006d1fe1ec2ea30e69fb97c"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.html#a490ec6b59006d1fe1ec2ea30e69fb97c">armnn::PermutationVector::GetSize</a></div><div class="ttdeci">SizeType GetSize() const</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.html#l00357">Types.hpp:357</a></div></div>
 <div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape &amp; GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00193">Tensor.hpp:193</a></div></div>
+<div class="ttc" id="anamespacearmnn_html_a75ca90884e15396a70b0cb722a877b4aa06933067aafd48425d67bcb01bba5cb6"><div class="ttname"><a href="namespacearmnn.html#a75ca90884e15396a70b0cb722a877b4aa06933067aafd48425d67bcb01bba5cb6">armnn::ScatterNdFunction::Update</a></div><div class="ttdeci">@ Update</div></div>
 <div class="ttc" id="anamespacearmnn_deserializer_html_a6713b8a83104db317823b5367b195d2e"><div class="ttname"><a href="namespacearmnn_deserializer.html#a6713b8a83104db317823b5367b195d2e">armnnDeserializer::Pooling3dDescriptor</a></div><div class="ttdeci">const armnnSerializer::Pooling3dDescriptor * Pooling3dDescriptor</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8hpp_source.html#l00022">Deserializer.hpp:22</a></div></div>
+<div class="ttc" id="anamespacearmnn_html_a75ca90884e15396a70b0cb722a877b4aa6a061313d22e51e0f25b7cd4dc065233"><div class="ttname"><a href="namespacearmnn.html#a75ca90884e15396a70b0cb722a877b4aa6a061313d22e51e0f25b7cd4dc065233">armnn::ScatterNdFunction::Max</a></div><div class="ttdeci">@ Max</div></div>
 <div class="ttc" id="a_exceptions_8hpp_html"><div class="ttname"><a href="_exceptions_8hpp.html">Exceptions.hpp</a></div></div>
 <div class="ttc" id="anamespacearmnn_html"><div class="ttname"><a href="namespacearmnn.html">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors.</div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.html#l00006">01_00_quick_start.dox:6</a></div></div>
 <div class="ttc" id="anamespacearmnn_html_aa5baabb8e3a4aa6cbdcab419d743e747"><div class="ttname"><a href="namespacearmnn.html#aa5baabb8e3a4aa6cbdcab419d743e747">armnn::ConvertNormalizationAlgorithmChannelToAclNormType</a></div><div class="ttdeci">arm_compute::NormType ConvertNormalizationAlgorithmChannelToAclNormType(NormalizationAlgorithmChannel channelType)</div><div class="ttdef"><b>Definition:</b> <a href="_arm_compute_utils_8hpp_source.html#l00182">ArmComputeUtils.hpp:182</a></div></div>
@@ -606,7 +635,7 @@
 <div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
   <ul>
     <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li><li class="navelem"><a class="el" href="dir_0f3cdec46afbc61a1ded8e1687c9c9a0.html">backends</a></li><li class="navelem"><a class="el" href="dir_c13beb47b846b3a63741c705c772cf8d.html">aclCommon</a></li><li class="navelem"><a class="el" href="_arm_compute_tensor_utils_8cpp.html">ArmComputeTensorUtils.cpp</a></li>
-    <li class="footer">Generated on Wed Feb 14 2024 16:36:15 for Arm NN by
+    <li class="footer">Generated on Thu May 16 2024 09:31:46 for Arm NN by
     <a href="http://www.doxygen.org/index.html">
     <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.17 </li>
   </ul>