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index 6d06ce3..f61adb8 100644
--- a/latest/_onnx_parser_8cpp_source.html
+++ b/latest/_onnx_parser_8cpp_source.html
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 <div class="contents">
 <a href="_onnx_parser_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">//</span></div>
-<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// Copyright © 2017,2022-2023 Arm Ltd and Contributors. All rights reserved.</span></div>
+<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// Copyright © 2017,2022-2024 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 &quot;<a class="code" href="_onnx_parser_8hpp.html">OnnxParser.hpp</a>&quot;</span></div>
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 <div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160; </div>
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-<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160; </div>
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-<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;}</div>
-<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160; </div>
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-<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;                               validInputTypes.end(),</div>
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+<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;                            <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div>
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+<div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;                std::accumulate(targetDims.begin(), targetDims.end(), -1, std::multiplies&lt;int32_t&gt;()));</div>
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+<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;            <span class="keywordflow">if</span> (inShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() == 0)</div>
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+<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;                outDims[stretchIndex] = 0;</div>
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+<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;            <span class="keywordflow">else</span></div>
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+<div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;                        fmt::format(<span class="stringliteral">&quot;Input to reshape is a tensor with elements, but the requested shape has 0. {}&quot;</span>,</div>
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+<div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;        }</div>
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+<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;            outDims[stretchIndex] = inShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / targetNumElements;</div>
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+<div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;    }</div>
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+<div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_info.html">TensorInfo</a>(outShape, dataType);</div>
+<div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;}</div>
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-<div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;<span class="keyword">const</span> std::map&lt;std::string, OnnxParserImpl::OperationParsingFunction&gt; OnnxParserImpl::m_ParserFunctions = {</div>
-<div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;    { <span class="stringliteral">&quot;BatchNormalization&quot;</span>,    &amp;OnnxParserImpl::ParseBatchNormalization},</div>
-<div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;    { <span class="stringliteral">&quot;GlobalAveragePool&quot;</span>,     &amp;OnnxParserImpl::ParseGlobalAveragePool},</div>
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-<div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;    { <span class="stringliteral">&quot;Clip&quot;</span>,                  &amp;OnnxParserImpl::ParseClip },</div>
-<div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;    { <span class="stringliteral">&quot;Constant&quot;</span>,              &amp;OnnxParserImpl::ParseConstant },</div>
-<div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;    { <span class="stringliteral">&quot;MaxPool&quot;</span>,               &amp;OnnxParserImpl::ParseMaxPool },</div>
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-<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;    { <span class="stringliteral">&quot;Sigmoid&quot;</span>,               &amp;OnnxParserImpl::ParseSigmoid },</div>
-<div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;    { <span class="stringliteral">&quot;Tanh&quot;</span>,                  &amp;OnnxParserImpl::ParseTanh },</div>
-<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;    { <span class="stringliteral">&quot;Relu&quot;</span>,                  &amp;OnnxParserImpl::ParseRelu },</div>
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-<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;    { <span class="stringliteral">&quot;Conv&quot;</span>,                  &amp;OnnxParserImpl::ParseConv },</div>
-<div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;    { <span class="stringliteral">&quot;Add&quot;</span>,                   &amp;OnnxParserImpl::ParseAdd },</div>
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-<div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160; </div>
-<div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;<span class="keywordtype">void</span> OnnxParserImpl::ResetParser()</div>
-<div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;{</div>
-<div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;    m_Network = <a class="code" href="namespacearmnn.html#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a>(<span class="keyword">nullptr</span>, <span class="keyword">nullptr</span>);</div>
-<div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;    m_Graph = <span class="keyword">nullptr</span>;</div>
-<div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;    m_InputInfos.clear();</div>
-<div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;    m_OutputInfos.clear();</div>
-<div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;}</div>
-<div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160; </div>
-<div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;<span class="keywordtype">void</span> OnnxParserImpl::Cleanup()</div>
-<div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;{</div>
-<div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;    m_TensorConnections.clear();</div>
-<div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;    m_TensorsInfo.clear();</div>
-<div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;    m_OutputsMap.clear();</div>
-<div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;    m_OutputsFusedAndUsed.clear();</div>
-<div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;    m_InputShapes.clear();</div>
-<div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;}</div>
-<div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160; </div>
-<div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> T&gt;</div>
-<div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;std::pair&lt;armnn::ConstTensor, std::unique_ptr&lt;T[]&gt;&gt;</div>
-<div class="line"><a name="l00602"></a><span class="lineno"><a class="line" href="namespacearmnn_onnx_parser.html#af6e5ebe4434a071057653025c4bb821b">  602</a></span>&#160;<a class="code" href="namespacearmnn_onnx_parser.html#af6e5ebe4434a071057653025c4bb821b">CreateConstTensorImpl</a>(<span class="keyword">const</span> T* bufferPtr,</div>
-<div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;                      <a class="code" href="classarmnn_1_1_tensor_info.html">armnn::TensorInfo</a>&amp; tensorInfo,</div>
-<div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160;                      <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.html">armnn::Optional&lt;armnn::PermutationVector&amp;&gt;</a> permutationVector)</div>
-<div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;{</div>
-<div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;    <span class="keywordflow">if</span> (bufferPtr == <span class="keyword">nullptr</span>)</div>
-<div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;    {</div>
-<div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">armnn::ParseException</a>(fmt::format(<span class="stringliteral">&quot;Buffer for permutation is null {}&quot;</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div>
-<div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;    }</div>
-<div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160; </div>
-<div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;    std::unique_ptr&lt;T[]&gt; data(<span class="keyword">new</span> T[tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>()]);</div>
+<div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;} <span class="comment">//namespace</span></div>
+<div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160; </div>
+<div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;<span class="keyword">const</span> std::map&lt;std::string, OnnxParserImpl::OperationParsingFunction&gt; OnnxParserImpl::m_ParserFunctions = {</div>
+<div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;    { <span class="stringliteral">&quot;BatchNormalization&quot;</span>,    &amp;OnnxParserImpl::ParseBatchNormalization},</div>
+<div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;    { <span class="stringliteral">&quot;GlobalAveragePool&quot;</span>,     &amp;OnnxParserImpl::ParseGlobalAveragePool},</div>
+<div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;    { <span class="stringliteral">&quot;AveragePool&quot;</span>,           &amp;OnnxParserImpl::ParseAveragePool },</div>
+<div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;    { <span class="stringliteral">&quot;Clip&quot;</span>,                  &amp;OnnxParserImpl::ParseClip },</div>
+<div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;    { <span class="stringliteral">&quot;Constant&quot;</span>,              &amp;OnnxParserImpl::ParseConstant },</div>
+<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;    { <span class="stringliteral">&quot;MaxPool&quot;</span>,               &amp;OnnxParserImpl::ParseMaxPool },</div>
+<div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;    { <span class="stringliteral">&quot;Reshape&quot;</span>,               &amp;OnnxParserImpl::ParseReshape },</div>
+<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;    { <span class="stringliteral">&quot;Sigmoid&quot;</span>,               &amp;OnnxParserImpl::ParseSigmoid },</div>
+<div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;    { <span class="stringliteral">&quot;Tanh&quot;</span>,                  &amp;OnnxParserImpl::ParseTanh },</div>
+<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;    { <span class="stringliteral">&quot;Relu&quot;</span>,                  &amp;OnnxParserImpl::ParseRelu },</div>
+<div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;    { <span class="stringliteral">&quot;LeakyRelu&quot;</span>,             &amp;OnnxParserImpl::ParseLeakyRelu },</div>
+<div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;    { <span class="stringliteral">&quot;Conv&quot;</span>,                  &amp;OnnxParserImpl::ParseConv },</div>
+<div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;    { <span class="stringliteral">&quot;Add&quot;</span>,                   &amp;OnnxParserImpl::ParseAdd },</div>
+<div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;    { <span class="stringliteral">&quot;Flatten&quot;</span>,               &amp;OnnxParserImpl::ParseFlatten },</div>
+<div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;    { <span class="stringliteral">&quot;Shape&quot;</span>,                 &amp;OnnxParserImpl::ParseShape },</div>
+<div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;    { <span class="stringliteral">&quot;Gather&quot;</span>,                &amp;OnnxParserImpl::ParseGather },</div>
+<div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;    { <span class="stringliteral">&quot;Unsqueeze&quot;</span>,             &amp;OnnxParserImpl::ParseUnsqueeze },</div>
+<div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;    { <span class="stringliteral">&quot;Concat&quot;</span>,                &amp;OnnxParserImpl::ParseConcat },</div>
+<div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;    { <span class="stringliteral">&quot;Gemm&quot;</span>,                  &amp;OnnxParserImpl::ParseGemm }</div>
+<div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;};</div>
+<div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160; </div>
+<div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> TypePair, <span class="keyword">typename</span> Location&gt;</div>
+<div class="line"><a name="l00496"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a74e612d0e7242695de575fb44e7f0762">  496</a></span>&#160;<span class="keywordtype">void</span> OnnxParserImpl::ValidateInputs(<span class="keyword">const</span> onnx::NodeProto&amp; node,</div>
+<div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;                                TypePair validInputs,</div>
+<div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;                                <span class="keyword">const</span> Location&amp; location)</div>
+<div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;{</div>
+<div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;    <span class="keywordflow">for</span>(<span class="keyword">auto</span> input : node.input())</div>
+<div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;    {</div>
+<div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;        CheckValidDataType(validInputs.second,</div>
+<div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;                           m_TensorsInfo[input].m_dtype,</div>
+<div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;                           validInputs.first,</div>
+<div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;                           node.name(),</div>
+<div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;                           input,</div>
+<div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;                           location);</div>
+<div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;    }</div>
+<div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;}</div>
+<div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160; </div>
+<div class="line"><a name="l00511"></a><span class="lineno"><a class="line" href="_onnx_parser_8cpp.html#a0e987f9d4f46b35c9b1ff0cc950dc5b1">  511</a></span>&#160;<span class="preprocessor">#define VALID_INPUTS(NODE, VALID_INPUTS) \</span></div>
+<div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;<span class="preprocessor">    OnnxParserImpl::ValidateInputs(NODE, \</span></div>
+<div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;<span class="preprocessor">                               VALID_INPUTS, \</span></div>
+<div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;<span class="preprocessor">                               CHECK_LOCATION())</span></div>
+<div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160; </div>
+<div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;std::vector&lt;TensorInfo&gt; OnnxParserImpl::ComputeOutputInfo(std::vector&lt;std::string&gt; outNames,</div>
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+<div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;                                                          <span class="keyword">const</span> <a class="code" href="namespacearmnn.html#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a>&amp; dataType)</div>
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+<div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160; </div>
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+<div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;                                   [<span class="keyword">this</span>](std::string name)</div>
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+<div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;                                       return (m_TensorsInfo.count(name) == 0 ||</div>
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+<div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;                                               m_TensorsInfo[name].m_info-&gt;GetShape().GetDimensionality() ==</div>
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+<div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;    <span class="comment">//if the output info(s) are not here, we need to compute them</span></div>
+<div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;    std::vector&lt;TensorShape&gt; inferredShapes;</div>
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+<div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;                armnnType = DataType::Signed32;</div>
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+<div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;            <span class="keywordflow">default</span>: {</div>
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+<div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;                                <span class="stringliteral">&quot; Supported dataTypes are FLOAT, INT32 and INT64.  {}&quot;</span>,</div>
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+<div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;                                layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>(),</div>
+<div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;                                <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div>
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+<div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160;            m_TensorsInfo[outNames[i]].m_dtype = dataType;</div>
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+<div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;}</div>
+<div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160; </div>
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+<div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;    : m_Network(nullptr, nullptr)</div>
+<div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;{</div>
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+<div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>&#160;    }</div>
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-<div class="line"><a name="l01707"></a><span class="lineno"> 1707</span>&#160; </div>
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+<div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>&#160;    {</div>
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+<div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(</div>
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+<div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>&#160;                            <span class="stringliteral">&quot;The &#39;group&#39;={} parameter cannot be larger than the &quot;</span></div>
+<div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>&#160;                            <span class="stringliteral">&quot;channel of the input shape={} (in NCHW format). {}&quot;</span>,</div>
+<div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>&#160;                            node.name(),</div>
+<div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>&#160;                            group,</div>
+<div class="line"><a name="l01917"></a><span class="lineno"> 1917</span>&#160;                            inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1],</div>
+<div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>&#160;                            <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div>
+<div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>&#160;        }</div>
+<div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span> (group == inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1])</div>
+<div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>&#160;        {</div>
+<div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>&#160;            <span class="comment">// we use a depthwise convolution here, because the number of groups equals to the</span></div>
+<div class="line"><a name="l01923"></a><span class="lineno"> 1923</span>&#160;            <span class="comment">// input channels</span></div>
+<div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>&#160;            AddConvLayerWithDepthwiseConv(node, desc);</div>
+<div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>&#160;            <span class="keywordflow">return</span>;</div>
+<div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>&#160;        }</div>
+<div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>&#160;        <span class="keywordflow">else</span></div>
+<div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>&#160;        {</div>
+<div class="line"><a name="l01929"></a><span class="lineno"> 1929</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">&quot;Error parsing Convolution node: {}. &quot;</span></div>
+<div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>&#160;                                             <span class="stringliteral">&quot;The &#39;group&#39;={} parameter should be 1 or be equal to the &quot;</span></div>
+<div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>&#160;                                             <span class="stringliteral">&quot;channel of the input shape={} (in NCHW format). {}&quot;</span>,</div>
+<div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>&#160;                                             node.name(),</div>
+<div class="line"><a name="l01933"></a><span class="lineno"> 1933</span>&#160;                                             group,</div>
+<div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>&#160;                                             inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.html#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1],</div>
+<div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>&#160;                                             <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div>
+<div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>&#160;        }</div>
+<div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>&#160;    }</div>
+<div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>&#160; </div>
+<div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>&#160;    node.input_size() == 3 ? desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = true : desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div>
+<div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.html">armnn::IConnectableLayer</a>* layer = m_Network-&gt;AddConvolution2dLayer(desc, node.name().c_str());</div>
+<div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>&#160;    std::vector&lt;std::string&gt; tensorIndexes= {node.input(0), node.input(1)};</div>
 <div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>&#160; </div>
-<div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* weightsLayer = m_Network-&gt;AddConstantLayer(weightTensor.first);</div>
-<div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>&#160;    weightsLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(weightTensor.first.GetInfo());</div>
-<div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>&#160;    weightsLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1u));</div>
-<div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>&#160; </div>
-<div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>&#160;    <span class="keywordflow">if</span> (node.input_size() == 3)</div>
-<div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>&#160;    {</div>
-<div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>&#160;        <span class="keywordflow">if</span>(!m_TensorsInfo[node.input(2)].isConstant())</div>
-<div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>&#160;        {</div>
-<div class="line"><a name="l01951"></a><span class="lineno"> 1951</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">&quot;Bias &#39;{}&#39; should be constant in Conv layer &#39;{}&#39; {}&quot;</span>,</div>
-<div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>&#160;                                             node.input(2),</div>
-<div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>&#160;                                             node.name(),</div>
-<div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>&#160;                                             <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div>
-<div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>&#160;        }</div>
-<div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>&#160;        desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div>
-<div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>&#160;        <span class="keyword">auto</span> biasTensor = CreateConstTensor(node.input(2));</div>
-<div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>&#160; </div>
-<div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* biasLayer = m_Network-&gt;AddConstantLayer(biasTensor.first);</div>
-<div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>&#160;        biasLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(biasTensor.first.GetInfo());</div>
-<div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>&#160;        biasLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2u));</div>
-<div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>&#160; </div>
-<div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>&#160;        tensorIndexes.emplace_back(node.input(2));</div>
-<div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>&#160;    }</div>
-<div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>&#160; </div>
-<div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>&#160;    <span class="keywordflow">if</span> (!layer)</div>
-<div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>&#160;    {</div>
-<div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">&quot;Layer pointer is null {}&quot;</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div>
-<div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>&#160;    }</div>
-<div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>&#160; </div>
-<div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>&#160;    <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({ node.output(0) }, layer,</div>
-<div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>&#160;                                        { m_TensorsInfo[node.input(0)].m_info-&gt;GetShape(),</div>
-<div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>&#160;                                          m_TensorsInfo[node.input(1)].m_info-&gt;GetShape() });</div>
-<div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>&#160;    layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div>
-<div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>&#160; </div>
-<div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>&#160;    <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div>
-<div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>&#160;    <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div>
-<div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>&#160;    RegisterInputSlots(layer, tensorIndexes);</div>
-<div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>&#160; </div>
-<div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>&#160;    <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div>
-<div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>&#160;    RegisterOutputSlots(layer, {node.output(0)});</div>
-<div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>&#160;}</div>
-<div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>&#160; </div>
-<div class="line"><a name="l01984"></a><span class="lineno"> 1984</span>&#160;<span class="keywordtype">void</span> OnnxParserImpl::ParseFlatten(<span class="keyword">const</span> onnx::NodeProto&amp; node)</div>
-<div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>&#160;{</div>
-<div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>&#160;    <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast&lt;</span><span class="keywordtype">size_t</span><span class="keyword">&gt;</span>(node.input_size()), 1);</div>
-<div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>&#160;    <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast&lt;</span><span class="keywordtype">size_t</span><span class="keyword">&gt;</span>(node.output_size()), 1);</div>
-<div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>&#160; </div>
-<div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>&#160;    <a class="code" href="_onnx_parser_8cpp.html#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a>(node.name(), node.input(0),</div>
-<div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>&#160;                         m_TensorsInfo[node.input(0)].m_dtype,</div>
-<div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>&#160;                         onnx::TensorProto::FLOAT);</div>
-<div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>&#160; </div>
-<div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>&#160;    int64_t axis = ReadOptionalNodeInt64Attribute(node, <span class="stringliteral">&quot;axis&quot;</span>, 1);</div>
-<div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> inputShape = m_TensorsInfo[node.input(0)].m_info-&gt;GetShape();</div>
-<div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>&#160;<span class="comment"></span> </div>
-<div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>&#160;<span class="comment">    /// Negative axis conversion</span></div>
-<div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>&#160;<span class="comment"></span>    <span class="keywordflow">if</span> (axis &lt; 0)</div>
-<div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>&#160;    {</div>
-<div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>&#160;        axis += inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div>
-<div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>&#160;    }</div>
-<div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>&#160;<span class="comment"></span> </div>
-<div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>&#160;<span class="comment">    /// Check Axis is within dimensions</span></div>
-<div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>&#160;<span class="comment"></span>    <span class="keywordflow">if</span> (axis &lt; 0 || axis &gt;= inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div>
-<div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>&#160;    {</div>
-<div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">&quot;Axis &#39;{}&#39; invalid. Tensor has &#39;{}&#39; dimensions in FlattenLayer &#39;{}&#39;&quot;</span>,</div>
-<div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>&#160;                                         axis, inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), node.name()));</div>
-<div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>&#160;    }</div>
-<div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>&#160;<span class="comment"></span> </div>
-<div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>&#160;<span class="comment">    /// If axis chosen is 0 dimension1 will always be 1 in output , default dimension2 to 1 because 0 is invalid</span></div>
-<div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>&#160;<span class="comment"></span>    uint dimension1{1};</div>
-<div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>&#160;    uint dimension2{1};</div>
-<div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>&#160;    uint i{0};</div>
-<div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>&#160;<span class="comment"></span> </div>
-<div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>&#160;<span class="comment">    /// dimension1 = (d_0 * d_1 ... d_(axis-1))</span></div>
-<div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>&#160;<span class="comment"></span>    <span class="keywordflow">for</span> (i = 0; i &lt; axis; i++){</div>
-<div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>&#160;        dimension1 *= inputShape[i];</div>
-<div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>&#160;    }</div>
-<div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>&#160;<span class="comment"></span> </div>
-<div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>&#160;<span class="comment">    /// dimension2 = (d_axis * d_(axis+1) ... d_n)</span></div>
-<div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>&#160;<span class="comment"></span>    <span class="keywordflow">for</span> (i = <span class="keyword">static_cast&lt;</span>uint<span class="keyword">&gt;</span>(axis); i &lt; inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++){</div>
-<div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>&#160;        dimension2 *= inputShape[i];</div>
-<div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>&#160;    }</div>
-<div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>&#160; </div>
-<div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> outputShape{dimension1, dimension2};</div>
+<div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>&#160;    <span class="keyword">auto</span> weightTensor = CreateConstTensor(node.input(1));</div>
+<div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>&#160; </div>
+<div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* weightsLayer = m_Network-&gt;AddConstantLayer(weightTensor.first);</div>
+<div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>&#160;    weightsLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(weightTensor.first.GetInfo());</div>
+<div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>&#160;    weightsLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1u));</div>
+<div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>&#160; </div>
+<div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>&#160;    <span class="keywordflow">if</span> (node.input_size() == 3)</div>
+<div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>&#160;    {</div>
+<div class="line"><a name="l01951"></a><span class="lineno"> 1951</span>&#160;        <span class="keywordflow">if</span>(!m_TensorsInfo[node.input(2)].isConstant())</div>
+<div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>&#160;        {</div>
+<div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>&#160;            <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">&quot;Bias &#39;{}&#39; should be constant in Conv layer &#39;{}&#39; {}&quot;</span>,</div>
+<div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>&#160;                                             node.input(2),</div>
+<div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>&#160;                                             node.name(),</div>
+<div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>&#160;                                             <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div>
+<div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>&#160;        }</div>
+<div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>&#160;        desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.html#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div>
+<div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>&#160;        <span class="keyword">auto</span> biasTensor = CreateConstTensor(node.input(2));</div>
+<div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>&#160; </div>
+<div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>&#160;        <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* biasLayer = m_Network-&gt;AddConstantLayer(biasTensor.first);</div>
+<div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>&#160;        biasLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(biasTensor.first.GetInfo());</div>
+<div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>&#160;        biasLayer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2u));</div>
+<div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>&#160; </div>
+<div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>&#160;        tensorIndexes.emplace_back(node.input(2));</div>
+<div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>&#160;    }</div>
+<div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>&#160; </div>
+<div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>&#160;    <span class="keywordflow">if</span> (!layer)</div>
+<div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>&#160;    {</div>
+<div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_null_pointer_exception.html">armnn::NullPointerException</a>(fmt::format(<span class="stringliteral">&quot;Layer pointer is null {}&quot;</span>, <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div>
+<div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>&#160;    }</div>
+<div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>&#160; </div>
+<div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>&#160;    <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({ node.output(0) }, layer,</div>
+<div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>&#160;                                        { m_TensorsInfo[node.input(0)].m_info-&gt;GetShape(),</div>
+<div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>&#160;                                          m_TensorsInfo[node.input(1)].m_info-&gt;GetShape() });</div>
+<div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>&#160;    layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div>
+<div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>&#160; </div>
+<div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>&#160;    <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div>
+<div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>&#160;    <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div>
+<div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>&#160;    RegisterInputSlots(layer, tensorIndexes);</div>
+<div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>&#160; </div>
+<div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>&#160;    <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div>
+<div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>&#160;    RegisterOutputSlots(layer, {node.output(0)});</div>
+<div class="line"><a name="l01984"></a><span class="lineno"> 1984</span>&#160;}</div>
+<div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>&#160; </div>
+<div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>&#160;<span class="keywordtype">void</span> OnnxParserImpl::ParseFlatten(<span class="keyword">const</span> onnx::NodeProto&amp; node)</div>
+<div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>&#160;{</div>
+<div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>&#160;    <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast&lt;</span><span class="keywordtype">size_t</span><span class="keyword">&gt;</span>(node.input_size()), 1);</div>
+<div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>&#160;    <a class="code" href="_verification_helpers_8hpp.html#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(<span class="keyword">static_cast&lt;</span><span class="keywordtype">size_t</span><span class="keyword">&gt;</span>(node.output_size()), 1);</div>
+<div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>&#160; </div>
+<div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>&#160;    <a class="code" href="_onnx_parser_8cpp.html#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a>(node.name(), node.input(0),</div>
+<div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>&#160;                         m_TensorsInfo[node.input(0)].m_dtype,</div>
+<div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>&#160;                         onnx::TensorProto::FLOAT);</div>
+<div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>&#160; </div>
+<div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>&#160;    int64_t axis = ReadOptionalNodeInt64Attribute(node, <span class="stringliteral">&quot;axis&quot;</span>, 1);</div>
+<div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> inputShape = m_TensorsInfo[node.input(0)].m_info-&gt;GetShape();</div>
+<div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>&#160;<span class="comment"></span> </div>
+<div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>&#160;<span class="comment">    /// Negative axis conversion</span></div>
+<div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>&#160;<span class="comment"></span>    <span class="keywordflow">if</span> (axis &lt; 0)</div>
+<div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>&#160;    {</div>
+<div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>&#160;        axis += inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div>
+<div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>&#160;    }</div>
+<div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>&#160;<span class="comment"></span> </div>
+<div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>&#160;<span class="comment">    /// Check Axis is within dimensions</span></div>
+<div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>&#160;<span class="comment"></span>    <span class="keywordflow">if</span> (axis &lt; 0 || axis &gt;= inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div>
+<div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>&#160;    {</div>
+<div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.html">ParseException</a>(fmt::format(<span class="stringliteral">&quot;Axis &#39;{}&#39; invalid. Tensor has &#39;{}&#39; dimensions in FlattenLayer &#39;{}&#39;&quot;</span>,</div>
+<div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>&#160;                                         axis, inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), node.name()));</div>
+<div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>&#160;    }</div>
+<div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>&#160;<span class="comment"></span> </div>
+<div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>&#160;<span class="comment">    /// If axis chosen is 0 dimension1 will always be 1 in output , default dimension2 to 1 because 0 is invalid</span></div>
+<div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>&#160;<span class="comment"></span>    uint dimension1{1};</div>
+<div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>&#160;    uint dimension2{1};</div>
+<div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>&#160;    uint i{0};</div>
+<div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>&#160;<span class="comment"></span> </div>
+<div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>&#160;<span class="comment">    /// dimension1 = (d_0 * d_1 ... d_(axis-1))</span></div>
+<div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>&#160;<span class="comment"></span>    <span class="keywordflow">for</span> (i = 0; i &lt; axis; i++){</div>
+<div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>&#160;        dimension1 *= inputShape[i];</div>
+<div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>&#160;    }</div>
+<div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>&#160;<span class="comment"></span> </div>
+<div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>&#160;<span class="comment">    /// dimension2 = (d_axis * d_(axis+1) ... d_n)</span></div>
+<div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>&#160;<span class="comment"></span>    <span class="keywordflow">for</span> (i = <span class="keyword">static_cast&lt;</span>uint<span class="keyword">&gt;</span>(axis); i &lt; inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++){</div>
+<div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>&#160;        dimension2 *= inputShape[i];</div>
+<div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>&#160;    }</div>
 <div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>&#160; </div>
-<div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>&#160;    <span class="keyword">auto</span> outInfo = ComputeReshapeInfo(outputShape, inputShape, node.output(0));</div>
-<div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>&#160;    m_TensorsInfo[node.output(0)].m_info = std::make_unique&lt;TensorInfo&gt;(outInfo);</div>
-<div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>&#160;    CreateReshapeLayer(node.input(0), node.output(0), node.name());</div>
-<div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>&#160;}</div>
-<div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>&#160; </div>
-<div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>&#160;<span class="keywordtype">void</span> OnnxParserImpl::ParseGather(<span class="keyword">const</span> onnx::NodeProto&amp; node)</div>
-<div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>&#160;{</div>
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-<div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>&#160; </div>
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-<div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>&#160; </div>
-<div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>&#160;    <a class="code" href="classarmnn_1_1_i_connectable_layer.html">IConnectableLayer</a>* layer = m_Network-&gt;AddGatherLayer(gatherDescriptor, node.name().c_str());</div>
+<div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.html">TensorShape</a> outputShape{dimension1, dimension2};</div>
+<div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>&#160; </div>
+<div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>&#160;    <span class="keyword">auto</span> outInfo = ComputeReshapeInfo(outputShape, inputShape, node.output(0));</div>
+<div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>&#160;    m_TensorsInfo[node.output(0)].m_info = std::make_unique&lt;TensorInfo&gt;(outInfo);</div>
+<div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>&#160;    CreateReshapeLayer(node.input(0), node.output(0), node.name());</div>
+<div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>&#160;}</div>
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+<div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>&#160;{</div>
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+<div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>&#160; </div>
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 <div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>&#160; </div>
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-<div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>&#160;    <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({node.output(0)}, layer, { inputShape, indicesShape },</div>
-<div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>&#160;                                        m_TensorsInfo[node.input(0)].m_dtype);</div>
-<div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>&#160;    layer-&gt;<a class="code" href="classarmnn_1_1_i_connectable_layer.html#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo[0]);</div>
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+<div class="line"><a name="l02674"></a><span class="lineno"> 2674</span>&#160;        isConstant[tensor.name()] = <span class="keyword">true</span>;</div>
+<div class="line"><a name="l02675"></a><span class="lineno"> 2675</span>&#160;    }</div>
+<div class="line"><a name="l02676"></a><span class="lineno"> 2676</span>&#160;    <span class="keywordflow">for</span>(<span class="keyword">auto</span> input : model-&gt;graph().input())</div>
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+<div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>&#160;        <span class="keyword">auto</span> it = isConstant.find(input.name());</div>
+<div class="line"><a name="l02679"></a><span class="lineno"> 2679</span>&#160;        <span class="keywordflow">if</span>(it == isConstant.end())</div>
+<div class="line"><a name="l02680"></a><span class="lineno"> 2680</span>&#160;        {</div>
+<div class="line"><a name="l02681"></a><span class="lineno"> 2681</span>&#160;            inputNames.push_back(input.name());</div>
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+<div class="line"><a name="l02683"></a><span class="lineno"> 2683</span>&#160;    }</div>
+<div class="line"><a name="l02684"></a><span class="lineno"> 2684</span>&#160;    <span class="keywordflow">return</span> inputNames;</div>
+<div class="line"><a name="l02685"></a><span class="lineno"> 2685</span>&#160;}</div>
+<div class="line"><a name="l02686"></a><span class="lineno"> 2686</span>&#160; </div>
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+<div class="line"><a name="l02688"></a><span class="lineno"> 2688</span>&#160;{</div>
+<div class="line"><a name="l02689"></a><span class="lineno"> 2689</span>&#160;    <span class="keywordflow">if</span>(model == <span class="keyword">nullptr</span>) {</div>
+<div class="line"><a name="l02690"></a><span class="lineno"> 2690</span>&#160;        <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.html">InvalidArgumentException</a>(fmt::format(<span class="stringliteral">&quot;The given model cannot be null {}&quot;</span>,</div>
+<div class="line"><a name="l02691"></a><span class="lineno"> 2691</span>&#160;                                                   <a class="code" href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div>
+<div class="line"><a name="l02692"></a><span class="lineno"> 2692</span>&#160;    }</div>
+<div class="line"><a name="l02693"></a><span class="lineno"> 2693</span>&#160; </div>
+<div class="line"><a name="l02694"></a><span class="lineno"> 2694</span>&#160;    std::vector&lt;std::string&gt; outputNames;</div>
+<div class="line"><a name="l02695"></a><span class="lineno"> 2695</span>&#160;    <span class="keywordflow">for</span>(<span class="keyword">auto</span> output : model-&gt;graph().output())</div>
+<div class="line"><a name="l02696"></a><span class="lineno"> 2696</span>&#160;    {</div>
+<div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>&#160;        outputNames.push_back(output.name());</div>
+<div class="line"><a name="l02698"></a><span class="lineno"> 2698</span>&#160;    }</div>
+<div class="line"><a name="l02699"></a><span class="lineno"> 2699</span>&#160;    <span class="keywordflow">return</span> outputNames;</div>
+<div class="line"><a name="l02700"></a><span class="lineno"> 2700</span>&#160;}</div>
+<div class="line"><a name="l02701"></a><span class="lineno"> 2701</span>&#160; </div>
+<div class="line"><a name="l02702"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aa09a8bb02eed50715082d8b7fccd2f8d"> 2702</a></span>&#160;<span class="keyword">const</span> std::string <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aa09a8bb02eed50715082d8b7fccd2f8d">OnnxParserImpl::GetVersion</a>()</div>
+<div class="line"><a name="l02703"></a><span class="lineno"> 2703</span>&#160;{</div>
+<div class="line"><a name="l02704"></a><span class="lineno"> 2704</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="include_2armnn_onnx_parser_2_version_8hpp.html#a91718cb27a114419c34ce33827e94321">ONNX_PARSER_VERSION</a>;</div>
+<div class="line"><a name="l02705"></a><span class="lineno"> 2705</span>&#160;}</div>
+<div class="line"><a name="l02706"></a><span class="lineno"> 2706</span>&#160; </div>
+<div class="line"><a name="l02707"></a><span class="lineno"> 2707</span>&#160;} <span class="comment">// namespace armnnOnnxParser</span></div>
 </div><!-- fragment --></div><!-- contents -->
 </div><!-- doc-content -->
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@@ -2809,17 +2811,17 @@
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+<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_ad116319e33228bc23ec505887d3eee4d"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#ad116319e33228bc23ec505887d3eee4d">armnnOnnxParser::OnnxParserImpl::GetOutputs</a></div><div class="ttdeci">static std::vector&lt; std::string &gt; GetOutputs(ModelPtr &amp;model)</div><div class="ttdoc">Retrieve outputs names.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l02687">OnnxParser.cpp:2687</a></div></div>
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-<div class="ttc" id="a_onnx_parser_8cpp_html_a0e987f9d4f46b35c9b1ff0cc950dc5b1"><div class="ttname"><a href="_onnx_parser_8cpp.html#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a></div><div class="ttdeci">#define VALID_INPUTS(NODE, VALID_INPUTS)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00509">OnnxParser.cpp:509</a></div></div>
+<div class="ttc" id="a_onnx_parser_8cpp_html_a0e987f9d4f46b35c9b1ff0cc950dc5b1"><div class="ttname"><a href="_onnx_parser_8cpp.html#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a></div><div class="ttdeci">#define VALID_INPUTS(NODE, VALID_INPUTS)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00511">OnnxParser.cpp:511</a></div></div>
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-<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a8b053a6c449d0814cc831c916c126668"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a8b053a6c449d0814cc831c916c126668">armnnOnnxParser::OnnxParserImpl::GetNetworkInputBindingInfo</a></div><div class="ttdeci">BindingPointInfo GetNetworkInputBindingInfo(const std::string &amp;name) const</div><div class="ttdoc">Retrieve binding info (layer id and tensor info) for the network input identified by the given layer ...</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l02623">OnnxParser.cpp:2623</a></div></div>
+<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a8b053a6c449d0814cc831c916c126668"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a8b053a6c449d0814cc831c916c126668">armnnOnnxParser::OnnxParserImpl::GetNetworkInputBindingInfo</a></div><div class="ttdeci">BindingPointInfo GetNetworkInputBindingInfo(const std::string &amp;name) const</div><div class="ttdoc">Retrieve binding info (layer id and tensor info) for the network input identified by the given layer ...</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l02625">OnnxParser.cpp:2625</a></div></div>
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@@ -2827,17 +2829,18 @@
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 <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="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_acf9c6119ceb99755bc1f86c5a325c796"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#acf9c6119ceb99755bc1f86c5a325c796">armnnOnnxParser::OnnxParserImpl::LoadModelFromBinaryFile</a></div><div class="ttdeci">static ModelPtr LoadModelFromBinaryFile(const char *fileName)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00811">OnnxParser.cpp:811</a></div></div>
+<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_acf9c6119ceb99755bc1f86c5a325c796"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#acf9c6119ceb99755bc1f86c5a325c796">armnnOnnxParser::OnnxParserImpl::LoadModelFromBinaryFile</a></div><div class="ttdeci">static ModelPtr LoadModelFromBinaryFile(const char *fileName)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00813">OnnxParser.cpp:813</a></div></div>
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 <div class="ttc" id="a_exceptions_8hpp_html_aa3be76aec4ce713822a5ea1ecbb7bc61"><div class="ttname"><a href="_exceptions_8hpp.html#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a></div><div class="ttdeci">#define CHECK_LOCATION()</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.html#l00203">Exceptions.hpp:203</a></div></div>
-<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a4b1fdcb1985af12dd1848a9ffa5d3271"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a4b1fdcb1985af12dd1848a9ffa5d3271">armnnOnnxParser::OnnxParserImpl::GetNetworkOutputBindingInfo</a></div><div class="ttdeci">BindingPointInfo GetNetworkOutputBindingInfo(const std::string &amp;name) const</div><div class="ttdoc">Retrieve binding info (layer id and tensor info) for the network output identified by the given layer...</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l02642">OnnxParser.cpp:2642</a></div></div>
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+<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a4b1fdcb1985af12dd1848a9ffa5d3271"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a4b1fdcb1985af12dd1848a9ffa5d3271">armnnOnnxParser::OnnxParserImpl::GetNetworkOutputBindingInfo</a></div><div class="ttdeci">BindingPointInfo GetNetworkOutputBindingInfo(const std::string &amp;name) const</div><div class="ttdoc">Retrieve binding info (layer id and tensor info) for the network output identified by the given layer...</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l02644">OnnxParser.cpp:2644</a></div></div>
+<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a32a96909bc8a8ee9076bd4d5c1028301"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a32a96909bc8a8ee9076bd4d5c1028301">armnnOnnxParser::OnnxParserImpl::CreateNetworkFromBinary</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromBinary(const std::vector&lt; uint8_t &gt; &amp;binaryContent)</div><div class="ttdoc">Create the network from a protobuf binary.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00775">OnnxParser.cpp:775</a></div></div>
 <div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::DepthwiseConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00692">Descriptors.hpp:692</a></div></div>
 <div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_html_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.html#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00576">Descriptors.hpp:576</a></div></div>
 <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">armnn::Pooling2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00411">Descriptors.hpp:411</a></div></div>
+<div class="ttc" id="a_deprecated_8hpp_html_ab66a241a0ed3ee89c866e777b035d0ed"><div class="ttname"><a href="_deprecated_8hpp.html#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_BEGIN</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.html#l00033">Deprecated.hpp:33</a></div></div>
 <div class="ttc" id="anamespacearmnn_utils_html_a523deabeb7d0a884028b35eebfd1cb6c"><div class="ttname"><a href="namespacearmnn_utils.html#a523deabeb7d0a884028b35eebfd1cb6c">armnnUtils::ProcessConcatInputTensorInfo</a></div><div class="ttdeci">void ProcessConcatInputTensorInfo(armnn::TensorInfo &amp;inputTensorInfo, armnn::OriginsDescriptor &amp;concatDescriptor, const unsigned int &amp;concatAxis, unsigned int inputIndex, unsigned int &amp;mergeDimOrigin)</div><div class="ttdef"><b>Definition:</b> <a href="_parser_helper_8cpp_source.html#l00019">ParserHelper.cpp:19</a></div></div>
-<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a30c0c947bb15e86ee6d535ecd934c0a6"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a30c0c947bb15e86ee6d535ecd934c0a6">armnnOnnxParser::OnnxParserImpl::CreateNetworkFromString</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromString(const std::string &amp;protoText)</div><div class="ttdoc">Create the network directly from protobuf text in a string. Useful for debugging/testing.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00874">OnnxParser.cpp:874</a></div></div>
+<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a30c0c947bb15e86ee6d535ecd934c0a6"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a30c0c947bb15e86ee6d535ecd934c0a6">armnnOnnxParser::OnnxParserImpl::CreateNetworkFromString</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromString(const std::string &amp;protoText)</div><div class="ttdoc">Create the network directly from protobuf text in a string. Useful for debugging/testing.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00876">OnnxParser.cpp:876</a></div></div>
 <div class="ttc" id="anamespacearmnn_utils_html_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.html#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &amp;dstShape, const armnn::PermutationVector &amp;mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.html#l00164">Permute.cpp:164</a></div></div>
 <div class="ttc" id="aclassarmnn_1_1_i_connectable_layer_html_aa6e3c075c888e7c16942a468a3aae33c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.html#aa6e3c075c888e7c16942a468a3aae33c">armnn::IConnectableLayer::InferOutputShapes</a></div><div class="ttdeci">virtual std::vector&lt; TensorShape &gt; InferOutputShapes(const std::vector&lt; TensorShape &gt; &amp;inputShapes) const =0</div><div class="ttdoc">Infer the shape of the output(s) based on the provided input shape(s)</div></div>
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@@ -2856,7 +2859,7 @@
 <div class="ttc" id="aclassarmnn_1_1_tensor_shape_html"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.html">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00020">Tensor.hpp:20</a></div></div>
 <div class="ttc" id="a_verification_helpers_8hpp_html"><div class="ttname"><a href="_verification_helpers_8hpp.html">VerificationHelpers.hpp</a></div></div>
 <div class="ttc" id="aclassarmnn_1_1_i_output_slot_html"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.html">armnn::IOutputSlot</a></div><div class="ttdoc">An output connection slot for a layer.</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.html#l00053">INetwork.hpp:53</a></div></div>
-<div class="ttc" id="a_onnx_parser_8cpp_html_a71cae957feb9162183d6f62fd549ffe1"><div class="ttname"><a href="_onnx_parser_8cpp.html#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a></div><div class="ttdeci">#define CHECK_VALID_DATATYPE(NODE, TENSOR, ACTUAL,...)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00127">OnnxParser.cpp:127</a></div></div>
+<div class="ttc" id="a_onnx_parser_8cpp_html_a71cae957feb9162183d6f62fd549ffe1"><div class="ttname"><a href="_onnx_parser_8cpp.html#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a></div><div class="ttdeci">#define CHECK_VALID_DATATYPE(NODE, TENSOR, ACTUAL,...)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00129">OnnxParser.cpp:129</a></div></div>
 <div class="ttc" id="astructarmnn_1_1_check_location_html"><div class="ttname"><a href="structarmnn_1_1_check_location.html">armnn::CheckLocation</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.html#l00014">Exceptions.hpp:14</a></div></div>
 <div class="ttc" id="aclassarmnn_1_1_tensor_shape_html_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.html#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorShape::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdoc">Function that returns the tensor rank.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00174">Tensor.cpp:174</a></div></div>
 <div class="ttc" id="a_verification_helpers_8hpp_html_aa693ef8620e450b6362938828002f2a6"><div class="ttname"><a href="_verification_helpers_8hpp.html#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a></div><div class="ttdeci">#define CHECKED_INT32(VALUE)</div><div class="ttdef"><b>Definition:</b> <a href="_verification_helpers_8hpp_source.html#l00030">VerificationHelpers.hpp:30</a></div></div>
@@ -2866,7 +2869,7 @@
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 <div class="ttc" id="aclassarmnn_1_1_i_output_slot_html_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.html#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &amp;tensorInfo)=0</div></div>
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+<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_aed935c554e4f6a4e7b9dcde057d00877"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aed935c554e4f6a4e7b9dcde057d00877">armnnOnnxParser::OnnxParserImpl::CreateNetworkFromBinaryFile</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromBinaryFile(const char *graphFile)</div><div class="ttdoc">Create the network from a protobuf binary file on disk.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00841">OnnxParser.cpp:841</a></div></div>
 <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>
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 <div class="ttc" id="astructarmnn_1_1_reshape_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_reshape_descriptor.html">armnn::ReshapeDescriptor</a></div><div class="ttdoc">A ReshapeDescriptor for the ReshapeLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01023">Descriptors.hpp:1023</a></div></div>
@@ -2880,32 +2883,33 @@
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 <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>
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-<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a8e30b9dff215c314959ca3145e939338"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a8e30b9dff215c314959ca3145e939338">armnnOnnxParser::OnnxParserImpl::LoadModelFromBinary</a></div><div class="ttdeci">static ModelPtr LoadModelFromBinary(const std::vector&lt; uint8_t &gt; &amp;binaryContent)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00789">OnnxParser.cpp:789</a></div></div>
+<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a8e30b9dff215c314959ca3145e939338"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a8e30b9dff215c314959ca3145e939338">armnnOnnxParser::OnnxParserImpl::LoadModelFromBinary</a></div><div class="ttdeci">static ModelPtr LoadModelFromBinary(const std::vector&lt; uint8_t &gt; &amp;binaryContent)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00791">OnnxParser.cpp:791</a></div></div>
 <div class="ttc" id="a_parser_helper_8hpp_html"><div class="ttname"><a href="_parser_helper_8hpp.html">ParserHelper.hpp</a></div></div>
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+<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a181f87cf45fdc9f040a41c985ce7f8cd"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a181f87cf45fdc9f040a41c985ce7f8cd">armnnOnnxParser::OnnxParserImpl::LoadModelFromString</a></div><div class="ttdeci">static ModelPtr LoadModelFromString(const std::string &amp;inputString)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00857">OnnxParser.cpp:857</a></div></div>
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 <div class="ttc" id="a_permute_8hpp_html"><div class="ttname"><a href="_permute_8hpp.html">Permute.hpp</a></div></div>
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 <div class="ttc" id="anamespacearmnn_html_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.html#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div><div class="ttdeci">@ info</div></div>
-<div class="ttc" id="a_onnx_parser_8cpp_html_a5426a7adb280d1739cc6d66fe9ac1b9c"><div class="ttname"><a href="_onnx_parser_8cpp.html#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a></div><div class="ttdeci">#define STR_LIST(...)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00131">OnnxParser.cpp:131</a></div></div>
-<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_aa09a8bb02eed50715082d8b7fccd2f8d"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aa09a8bb02eed50715082d8b7fccd2f8d">armnnOnnxParser::OnnxParserImpl::GetVersion</a></div><div class="ttdeci">static const std::string GetVersion()</div><div class="ttdoc">Retrieve version in X.Y.Z form.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l02700">OnnxParser.cpp:2700</a></div></div>
+<div class="ttc" id="a_onnx_parser_8cpp_html_a5426a7adb280d1739cc6d66fe9ac1b9c"><div class="ttname"><a href="_onnx_parser_8cpp.html#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a></div><div class="ttdeci">#define STR_LIST(...)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00133">OnnxParser.cpp:133</a></div></div>
+<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_aa09a8bb02eed50715082d8b7fccd2f8d"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aa09a8bb02eed50715082d8b7fccd2f8d">armnnOnnxParser::OnnxParserImpl::GetVersion</a></div><div class="ttdeci">static const std::string GetVersion()</div><div class="ttdoc">Retrieve version in X.Y.Z form.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l02702">OnnxParser.cpp:2702</a></div></div>
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 <div class="ttc" id="astructarmnn_1_1_convolution2d_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.html">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00534">Descriptors.hpp:534</a></div></div>
 <div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#aa9e49717ebdb741e8c767741647fc618">armnn::DepthwiseConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00698">Descriptors.hpp:698</a></div></div>
 <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">armnn::Pooling2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00419">Descriptors.hpp:419</a></div></div>
-<div class="ttc" id="anamespacearmnn_deserializer_html_a948b8c615ff06defa3b80d2352259ff2"><div class="ttname"><a href="namespacearmnn_deserializer.html#a948b8c615ff06defa3b80d2352259ff2">armnnDeserializer::ToTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo ToTensorInfo(TensorRawPtr tensorPtr)</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8cpp_source.html#l00654">Deserializer.cpp:654</a></div></div>
+<div class="ttc" id="anamespacearmnn_deserializer_html_a948b8c615ff06defa3b80d2352259ff2"><div class="ttname"><a href="namespacearmnn_deserializer.html#a948b8c615ff06defa3b80d2352259ff2">armnnDeserializer::ToTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo ToTensorInfo(TensorRawPtr tensorPtr)</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8cpp_source.html#l00676">Deserializer.cpp:676</a></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="astructarmnn_1_1_transpose_descriptor_html_a14433af2b223695b40d8c8f8ba2ebb8f"><div class="ttname"><a href="structarmnn_1_1_transpose_descriptor.html#a14433af2b223695b40d8c8f8ba2ebb8f">armnn::TransposeDescriptor::m_DimMappings</a></div><div class="ttdeci">PermutationVector m_DimMappings</div><div class="ttdoc">Indicates how to translate tensor elements from a given source into the target destination,...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l01514">Descriptors.hpp:1514</a></div></div>
-<div class="ttc" id="anamespacearmnn_onnx_parser_html_af6e5ebe4434a071057653025c4bb821b"><div class="ttname"><a href="namespacearmnn_onnx_parser.html#af6e5ebe4434a071057653025c4bb821b">armnnOnnxParser::CreateConstTensorImpl</a></div><div class="ttdeci">std::pair&lt; armnn::ConstTensor, std::unique_ptr&lt; T[]&gt; &gt; CreateConstTensorImpl(const T *bufferPtr, armnn::TensorInfo &amp;tensorInfo, const armnn::Optional&lt; armnn::PermutationVector &amp; &gt; permutationVector)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00602">OnnxParser.cpp:602</a></div></div>
+<div class="ttc" id="anamespacearmnn_onnx_parser_html_af6e5ebe4434a071057653025c4bb821b"><div class="ttname"><a href="namespacearmnn_onnx_parser.html#af6e5ebe4434a071057653025c4bb821b">armnnOnnxParser::CreateConstTensorImpl</a></div><div class="ttdeci">std::pair&lt; armnn::ConstTensor, std::unique_ptr&lt; T[]&gt; &gt; CreateConstTensorImpl(const T *bufferPtr, armnn::TensorInfo &amp;tensorInfo, const armnn::Optional&lt; armnn::PermutationVector &amp; &gt; permutationVector)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00604">OnnxParser.cpp:604</a></div></div>
 <div class="ttc" id="aclassarmnn_1_1_i_output_slot_html_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.html#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &amp;destination)=0</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="astructarmnn_1_1_convolution2d_descriptor_html_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.html#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::Convolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation along x axis.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00578">Descriptors.hpp:578</a></div></div>
+<div class="ttc" id="a_deprecated_8hpp_html_ad762b11b48e5c1d1c1743f529485728a"><div class="ttname"><a href="_deprecated_8hpp.html#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_END</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.html#l00034">Deprecated.hpp:34</a></div></div>
 <div class="ttc" id="anamespacestd_html"><div class="ttname"><a href="namespacestd.html">std</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.html#l00149">BackendId.hpp:149</a></div></div>
 <div class="ttc" id="aclassarmnn_1_1_parse_exception_html"><div class="ttname"><a href="classarmnn_1_1_parse_exception.html">armnn::ParseException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.html#l00092">Exceptions.hpp:92</a></div></div>
 <div class="ttc" id="aclassarmnn_onnx_parser_1_1_i_onnx_parser_html"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_i_onnx_parser.html">armnnOnnxParser::IOnnxParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_onnx_parser_8hpp_source.html#l00023">IOnnxParser.hpp:23</a></div></div>
 <div class="ttc" id="anamespacearmnn_onnx_parser_html_a9084adbf804022c874039ad40d1939e9"><div class="ttname"><a href="namespacearmnn_onnx_parser.html#a9084adbf804022c874039ad40d1939e9">armnnOnnxParser::BindingPointInfo</a></div><div class="ttdeci">armnn::BindingPointInfo BindingPointInfo</div><div class="ttdef"><b>Definition:</b> <a href="_i_onnx_parser_8hpp_source.html#l00017">IOnnxParser.hpp:17</a></div></div>
-<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_aaf4ce461aa35597cf80954314a3cb0e1"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aaf4ce461aa35597cf80954314a3cb0e1">armnnOnnxParser::OnnxParserImpl::CreateNetworkFromTextFile</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromTextFile(const char *graphFile)</div><div class="ttdoc">Create the network from a protobuf text file on disk.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00757">OnnxParser.cpp:757</a></div></div>
+<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_aaf4ce461aa35597cf80954314a3cb0e1"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#aaf4ce461aa35597cf80954314a3cb0e1">armnnOnnxParser::OnnxParserImpl::CreateNetworkFromTextFile</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromTextFile(const char *graphFile)</div><div class="ttdoc">Create the network from a protobuf text file on disk.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00759">OnnxParser.cpp:759</a></div></div>
 <div class="ttc" id="anamespacearmnn_onnx_parser_html_ac7dfccab29feeb5f33f1ec0183c1e123"><div class="ttname"><a href="namespacearmnn_onnx_parser.html#ac7dfccab29feeb5f33f1ec0183c1e123">armnnOnnxParser::IOnnxParserPtr</a></div><div class="ttdeci">std::unique_ptr&lt; IOnnxParser, void(*)(IOnnxParser *parser)&gt; IOnnxParserPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_onnx_parser_8hpp_source.html#l00021">IOnnxParser.hpp:21</a></div></div>
 <div class="ttc" id="astructarmnn_1_1_origins_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.html">armnn::OriginsDescriptor</a></div><div class="ttdoc">An OriginsDescriptor for the ConcatLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00201">Descriptors.hpp:201</a></div></div>
 <div class="ttc" id="aclassarmnn_1_1_tensor_info_html_abe8889e8150beef5fd204b2d87b49298"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#abe8889e8150beef5fd204b2d87b49298">armnn::TensorInfo::SetShape</a></div><div class="ttdeci">void SetShape(const TensorShape &amp;newShape)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.html#l00195">Tensor.hpp:195</a></div></div>
@@ -2918,8 +2922,8 @@
 <div class="ttc" id="aclassarmnn_1_1_i_connectable_layer_html"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.html">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots.</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.html#l00080">INetwork.hpp:80</a></div></div>
 <div class="ttc" id="aclassarmnn_1_1_i_input_slot_html"><div class="ttname"><a href="classarmnn_1_1_i_input_slot.html">armnn::IInputSlot</a></div><div class="ttdoc">An input connection slot for a layer.</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.html#l00025">INetwork.hpp:25</a></div></div>
 <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html_affb5b68b3eba3ed45a06c7cde7781962"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html#affb5b68b3eba3ed45a06c7cde7781962">armnn::Pooling2dDescriptor::m_OutputShapeRounding</a></div><div class="ttdeci">OutputShapeRounding m_OutputShapeRounding</div><div class="ttdoc">The rounding method for the output shape. (Floor, Ceiling).</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00423">Descriptors.hpp:423</a></div></div>
-<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a7cf8b801043e1eccd5e6db1325eaa4fe"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a7cf8b801043e1eccd5e6db1325eaa4fe">armnnOnnxParser::OnnxParserImpl::GetInputs</a></div><div class="ttdeci">static std::vector&lt; std::string &gt; GetInputs(ModelPtr &amp;model)</div><div class="ttdoc">Retrieve inputs names.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l02661">OnnxParser.cpp:2661</a></div></div>
-<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a8ffca1e21bdfa7f945617acd606aac91"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a8ffca1e21bdfa7f945617acd606aac91">armnn::TensorInfo::SetConstant</a></div><div class="ttdeci">void SetConstant(const bool IsConstant=true)</div><div class="ttdoc">Marks the data corresponding to this tensor info as constant.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00514">Tensor.cpp:514</a></div></div>
+<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a7cf8b801043e1eccd5e6db1325eaa4fe"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a7cf8b801043e1eccd5e6db1325eaa4fe">armnnOnnxParser::OnnxParserImpl::GetInputs</a></div><div class="ttdeci">static std::vector&lt; std::string &gt; GetInputs(ModelPtr &amp;model)</div><div class="ttdoc">Retrieve inputs names.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l02663">OnnxParser.cpp:2663</a></div></div>
+<div class="ttc" id="aclassarmnn_1_1_tensor_info_html_a8ffca1e21bdfa7f945617acd606aac91"><div class="ttname"><a href="classarmnn_1_1_tensor_info.html#a8ffca1e21bdfa7f945617acd606aac91">armnn::TensorInfo::SetConstant</a></div><div class="ttdeci">void SetConstant(const bool IsConstant=true)</div><div class="ttdoc">Marks the data corresponding to this tensor info as constant.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.html#l00518">Tensor.cpp:518</a></div></div>
 <div class="ttc" id="astructarmnn_1_1_pooling2d_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.html">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00371">Descriptors.hpp:371</a></div></div>
 <div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00659">Descriptors.hpp:659</a></div></div>
 <div class="ttc" id="astructarmnn_1_1_batch_normalization_descriptor_html_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.html#a11c821c7524251004a72ed13c510853c">armnn::BatchNormalizationDescriptor::m_Eps</a></div><div class="ttdeci">float m_Eps</div><div class="ttdoc">Value to add to the variance. Used to avoid dividing by zero.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00841">Descriptors.hpp:841</a></div></div>
@@ -2931,15 +2935,15 @@
 <div class="ttc" id="aclassarmnn_1_1_optional_base_html_a86b749ce2c4bc627fa8a1fcfaf0e314f"><div class="ttname"><a href="classarmnn_1_1_optional_base.html#a86b749ce2c4bc627fa8a1fcfaf0e314f">armnn::OptionalBase::has_value</a></div><div class="ttdeci">bool has_value() const noexcept</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.html#l00053">Optional.hpp:53</a></div></div>
 <div class="ttc" id="aclassarmnn_1_1_file_not_found_exception_html"><div class="ttname"><a href="classarmnn_1_1_file_not_found_exception.html">armnn::FileNotFoundException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.html#l00086">Exceptions.hpp:86</a></div></div>
 <div class="ttc" id="anamespacearmnn_deserializer_html_a7e75f47f676327bce37149932aa4a011"><div class="ttname"><a href="namespacearmnn_deserializer.html#a7e75f47f676327bce37149932aa4a011">armnnDeserializer::Pooling2dDescriptor</a></div><div class="ttdeci">const armnnSerializer::Pooling2dDescriptor * Pooling2dDescriptor</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8hpp_source.html#l00021">Deserializer.hpp:21</a></div></div>
-<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a975a79b9b35d51ea81c42c05d245e7c0"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a975a79b9b35d51ea81c42c05d245e7c0">armnnOnnxParser::OnnxParserImpl::LoadModelFromTextFile</a></div><div class="ttdeci">static ModelPtr LoadModelFromTextFile(const char *fileName)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00732">OnnxParser.cpp:732</a></div></div>
+<div class="ttc" id="aclassarmnn_onnx_parser_1_1_onnx_parser_impl_html_a975a79b9b35d51ea81c42c05d245e7c0"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser_impl.html#a975a79b9b35d51ea81c42c05d245e7c0">armnnOnnxParser::OnnxParserImpl::LoadModelFromTextFile</a></div><div class="ttdeci">static ModelPtr LoadModelFromTextFile(const char *fileName)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00734">OnnxParser.cpp:734</a></div></div>
 <div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00700">Descriptors.hpp:700</a></div></div>
 <div class="ttc" id="astructarmnn_1_1_depthwise_convolution2d_descriptor_html_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.html#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.html#l00696">Descriptors.hpp:696</a></div></div>
-<div class="ttc" id="aclassarmnn_onnx_parser_1_1_i_onnx_parser_html_a6bf5861864c8828e59df24a7868c5439"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#a6bf5861864c8828e59df24a7868c5439">armnnOnnxParser::IOnnxParser::CreateNetworkFromBinaryFile</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromBinaryFile(const char *graphFile)</div><div class="ttdoc">Create the network from a protobuf binary file on disk.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00048">OnnxParser.cpp:48</a></div></div>
+<div class="ttc" id="aclassarmnn_onnx_parser_1_1_i_onnx_parser_html_a6bf5861864c8828e59df24a7868c5439"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_i_onnx_parser.html#a6bf5861864c8828e59df24a7868c5439">armnnOnnxParser::IOnnxParser::CreateNetworkFromBinaryFile</a></div><div class="ttdeci">armnn::INetworkPtr CreateNetworkFromBinaryFile(const char *graphFile)</div><div class="ttdoc">Create the network from a protobuf binary file on disk.</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.html#l00050">OnnxParser.cpp:50</a></div></div>
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