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<div class="title">TensorShape.h</div> </div>
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<a href="_tensor_shape_8h.xhtml">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 (c) 2016, 2017 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;<span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;<span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="preprocessor">#ifndef __ARM_COMPUTE_TENSORSHAPE_H__</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;<span class="preprocessor">#define __ARM_COMPUTE_TENSORSHAPE_H__</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_dimensions_8h.xhtml">arm_compute/core/Dimensions.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_error_8h.xhtml">arm_compute/core/Error.h</a>&quot;</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;<span class="preprocessor">#include &lt;algorithm&gt;</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;<span class="preprocessor">#include &lt;array&gt;</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;<span class="preprocessor">#include &lt;functional&gt;</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;<span class="preprocessor">#include &lt;numeric&gt;</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;{</div><div class="line"><a name="l00038"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml"> 38</a></span>&#160;<span class="keyword">class </span><a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1_dimensions.xhtml">Dimensions</a>&lt;size_t&gt;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160;{</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; <span class="keyword">template</span> &lt;<span class="keyword">typename</span>... Ts&gt;</div><div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#a929d0b5223357298ada0ce4d42fa5ec7"> 46</a></span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a929d0b5223357298ada0ce4d42fa5ec7">TensorShape</a>(Ts... dims)</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; : <a class="code" href="classarm__compute_1_1_dimensions.xhtml">Dimensions</a>{ dims... }</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; {</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; <span class="comment">// Initialize unspecified dimensions to 1</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; <span class="keywordflow">if</span>(_num_dimensions &gt; 0)</div><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; std::fill(_id.begin() + _num_dimensions, _id.end(), 1);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; }</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160;</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; <span class="comment">// Correct number dimensions to ignore trailing dimensions of size 1</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; apply_dimension_correction();</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; }</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a929d0b5223357298ada0ce4d42fa5ec7">TensorShape</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;) = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a355b1a84ab7af3b8ef9a6bea1939450a">operator=</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;) = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a929d0b5223357298ada0ce4d42fa5ec7">TensorShape</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;&amp;) = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a355b1a84ab7af3b8ef9a6bea1939450a">operator=</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &amp;&amp;) = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a29ff524f0e3378fb25a8447bdeed6ba9">~TensorShape</a>() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160;</div><div class="line"><a name="l00074"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#a0cb0e1f5da2e1cc2e0ea5690450f53e8"> 74</a></span>&#160; <span class="keywordtype">void</span> <span class="keyword">set</span>(<span class="keywordtype">size_t</span> dimension, <span class="keywordtype">size_t</span> value)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; {</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(value &lt; 1);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <span class="comment">// Make sure all empty dimensions are filled with 1</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; std::fill(_id.begin() + _num_dimensions, _id.end(), 1);</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; <span class="comment">// Set the specified dimension and increase the number of dimensions if</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; <span class="comment">// necessary</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; <a class="code" href="classarm__compute_1_1_dimensions.xhtml#a982730e6f0da5f9490f59bc5f6bb3f27">Dimensions::set</a>(dimension, value);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; <span class="comment">// Correct number dimensions to ignore trailing dimensions of size 1</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; apply_dimension_correction();</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; }</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160;</div><div class="line"><a name="l00094"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99"> 94</a></span>&#160; <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99">collapse</a>(<span class="keywordtype">size_t</span> n, <span class="keywordtype">size_t</span> first = 0)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; {</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; <a class="code" href="classarm__compute_1_1_dimensions.xhtml#a8e15e87871211f98c2b566137e38ef99">Dimensions::collapse</a>(n, first);</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; <span class="comment">// Make sure all empty dimensions are filled with 1</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; std::fill(_id.begin() + _num_dimensions, _id.end(), 1);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; }</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160;</div><div class="line"><a name="l00106"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#a4eaec01ba2c12093db609d1034ad0bc1"> 106</a></span>&#160; <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a4eaec01ba2c12093db609d1034ad0bc1">total_size</a>()<span class="keyword"> const</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; <span class="keywordflow">return</span> <a class="code" href="accumulate_8cl.xhtml#a00e540076dd545ad59ac7482f8cdf514">std::accumulate</a>(_id.begin(), _id.end(), 1, std::multiplies&lt;size_t&gt;());</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; }</div><div class="line"><a name="l00118"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_tensor_shape.xhtml#a91d8061f66e7f8bc56da91d965f04376"> 118</a></span>&#160; <span class="keywordtype">size_t</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a91d8061f66e7f8bc56da91d965f04376">total_size_upper</a>(<span class="keywordtype">size_t</span> dimension)<span class="keyword"> const</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="keywordflow">return</span> <a class="code" href="accumulate_8cl.xhtml#a00e540076dd545ad59ac7482f8cdf514">std::accumulate</a>(_id.begin() + dimension, _id.end(), 1, std::multiplies&lt;size_t&gt;());</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; }</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; <span class="keywordtype">void</span> apply_dimension_correction()</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; {</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = static_cast&lt;int&gt;(_num_dimensions) - 1; i &gt;= 0; --i)</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; {</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; <span class="keywordflow">if</span>(_id[i] == 1)</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; {</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; --_num_dimensions;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; }</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; {</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; <span class="keywordflow">break</span>;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; }</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; }</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; }</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160;};</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160;}</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160;<span class="preprocessor">#endif </span><span class="comment">/*__ARM_COMPUTE_TENSORSHAPE_H__*/</span><span class="preprocessor"></span></div><div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00038">TensorShape.h:38</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a355b1a84ab7af3b8ef9a6bea1939450a"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a355b1a84ab7af3b8ef9a6bea1939450a">arm_compute::TensorShape::operator=</a></div><div class="ttdeci">TensorShape &amp; operator=(const TensorShape &amp;)=default</div><div class="ttdoc">Allow instances of this class to be copied. </div></div>
<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00124">Error.h:124</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a982730e6f0da5f9490f59bc5f6bb3f27"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a982730e6f0da5f9490f59bc5f6bb3f27">arm_compute::Dimensions::set</a></div><div class="ttdeci">void set(size_t dimension, T value)</div><div class="ttdoc">Accessor to set the value of one of the dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00074">Dimensions.h:74</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdef"><b>Definition:</b> <a href="01__library_8dox_source.xhtml#l00001">01_library.dox:1</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a91d8061f66e7f8bc56da91d965f04376"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a91d8061f66e7f8bc56da91d965f04376">arm_compute::TensorShape::total_size_upper</a></div><div class="ttdeci">size_t total_size_upper(size_t dimension) const </div><div class="ttdoc">Collapses given dimension and above. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00118">TensorShape.h:118</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml">arm_compute::Dimensions</a></div><div class="ttdoc">Dimensions with dimensionality. </div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00041">Dimensions.h:41</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a929d0b5223357298ada0ce4d42fa5ec7"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a929d0b5223357298ada0ce4d42fa5ec7">arm_compute::TensorShape::TensorShape</a></div><div class="ttdeci">TensorShape(Ts...dims)</div><div class="ttdoc">Constructor to initialize the tensor shape. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00046">TensorShape.h:46</a></div></div>
<div class="ttc" id="_error_8h_xhtml"><div class="ttname"><a href="_error_8h.xhtml">Error.h</a></div></div>
<div class="ttc" id="_dimensions_8h_xhtml"><div class="ttname"><a href="_dimensions_8h.xhtml">Dimensions.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a4eaec01ba2c12093db609d1034ad0bc1"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a4eaec01ba2c12093db609d1034ad0bc1">arm_compute::TensorShape::total_size</a></div><div class="ttdeci">size_t total_size() const </div><div class="ttdoc">Collapses all dimensions to a single linear total size. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00106">TensorShape.h:106</a></div></div>
<div class="ttc" id="accumulate_8cl_xhtml_a00e540076dd545ad59ac7482f8cdf514"><div class="ttname"><a href="accumulate_8cl.xhtml#a00e540076dd545ad59ac7482f8cdf514">accumulate</a></div><div class="ttdeci">__kernel void accumulate(__global uchar *input_ptr, uint input_stride_x, uint input_step_x, uint input_stride_y, uint input_step_y, uint input_offset_first_element_in_bytes, __global uchar *accu_ptr, uint accu_stride_x, uint accu_step_x, uint accu_stride_y, uint accu_step_y, uint accu_offset_first_element_in_bytes)</div><div class="ttdoc">This function accumulates an input image into output image. </div><div class="ttdef"><b>Definition:</b> <a href="accumulate_8cl_source.xhtml#l00041">accumulate.cl:41</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a8e15e87871211f98c2b566137e38ef99"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a8e15e87871211f98c2b566137e38ef99">arm_compute::Dimensions::collapse</a></div><div class="ttdeci">void collapse(size_t n, size_t first=0)</div><div class="ttdoc">Collapse dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00125">Dimensions.h:125</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a29ff524f0e3378fb25a8447bdeed6ba9"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a29ff524f0e3378fb25a8447bdeed6ba9">arm_compute::TensorShape::~TensorShape</a></div><div class="ttdeci">~TensorShape()=default</div><div class="ttdoc">Default destructor. </div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a8e15e87871211f98c2b566137e38ef99"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a8e15e87871211f98c2b566137e38ef99">arm_compute::TensorShape::collapse</a></div><div class="ttdeci">void collapse(size_t n, size_t first=0)</div><div class="ttdoc">Collapse the first n dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00094">TensorShape.h:94</a></div></div>
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