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<h1>Source code for mxnet.ndarray.utils</h1><div class="highlight"><pre>
<span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span>
<span class="c1"># or more contributor license agreements. See the NOTICE file</span>
<span class="c1"># distributed with this work for additional information</span>
<span class="c1"># regarding copyright ownership. The ASF licenses this file</span>
<span class="c1"># to you under the Apache License, Version 2.0 (the</span>
<span class="c1"># "License"); you may not use this file except in compliance</span>
<span class="c1"># with the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing,</span>
<span class="c1"># software distributed under the License is distributed on an</span>
<span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span>
<span class="c1"># KIND, either express or implied. See the License for the</span>
<span class="c1"># specific language governing permissions and limitations</span>
<span class="c1"># under the License.</span>
<span class="c1"># coding: utf-8</span>
<span class="sd">"""Utility functions for NDArray and BaseSparseNDArray."""</span>
<span class="kn">import</span> <span class="nn">ctypes</span>
<span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">_LIB</span><span class="p">,</span> <span class="n">check_call</span><span class="p">,</span> <span class="n">py_str</span><span class="p">,</span> <span class="n">c_str</span><span class="p">,</span> <span class="n">string_types</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">,</span> <span class="n">NDArrayHandle</span>
<span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">c_array</span><span class="p">,</span> <span class="n">c_handle_array</span><span class="p">,</span> <span class="n">c_str_array</span>
<span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">NDArray</span>
<span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">array</span> <span class="k">as</span> <span class="n">_array</span>
<span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">empty</span> <span class="k">as</span> <span class="n">_empty_ndarray</span>
<span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">zeros</span> <span class="k">as</span> <span class="n">_zeros_ndarray</span>
<span class="kn">from</span> <span class="nn">.sparse</span> <span class="kn">import</span> <span class="n">zeros</span> <span class="k">as</span> <span class="n">_zeros_sparse_ndarray</span>
<span class="kn">from</span> <span class="nn">.sparse</span> <span class="kn">import</span> <span class="n">empty</span> <span class="k">as</span> <span class="n">_empty_sparse_ndarray</span>
<span class="kn">from</span> <span class="nn">.sparse</span> <span class="kn">import</span> <span class="n">array</span> <span class="k">as</span> <span class="n">_sparse_array</span>
<span class="kn">from</span> <span class="nn">.sparse</span> <span class="kn">import</span> <span class="n">_ndarray_cls</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">scipy.sparse</span> <span class="kn">as</span> <span class="nn">spsp</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="n">spsp</span> <span class="o">=</span> <span class="bp">None</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="s1">'empty'</span><span class="p">,</span> <span class="s1">'array'</span><span class="p">,</span> <span class="s1">'load'</span><span class="p">,</span> <span class="s1">'save'</span><span class="p">]</span>
<div class="viewcode-block" id="zeros"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.zeros">[docs]</a><span class="k">def</span> <span class="nf">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Return a new array of given shape and type, filled with zeros.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> shape : int or tuple of int</span>
<span class="sd"> The shape of the empty array</span>
<span class="sd"> ctx : Context, optional</span>
<span class="sd"> An optional device context (default is the current default context)</span>
<span class="sd"> dtype : str or numpy.dtype, optional</span>
<span class="sd"> An optional value type (default is `float32`)</span>
<span class="sd"> stype: string, optional</span>
<span class="sd"> The storage type of the empty array, such as 'row_sparse', 'csr', etc.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> NDArray, CSRNDArray or RowSparseNDArray</span>
<span class="sd"> A created array</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> mx.nd.zeros((1,2), mx.cpu(), stype='csr')</span>
<span class="sd"> <csrndarray 1x2="" @cpu(0)=""></csrndarray></span>
<span class="sd"> >>> mx.nd.zeros((1,2), mx.cpu(), 'float16', stype='row_sparse').asnumpy()</span>
<span class="sd"> array([[ 0., 0.]], dtype=float16)</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">stype</span> <span class="ow">is</span> <span class="bp">None</span> <span class="ow">or</span> <span class="n">stype</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">:</span>
<span class="k">return</span> <span class="n">_zeros_ndarray</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">_zeros_sparse_ndarray</span><span class="p">(</span><span class="n">stype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="empty"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.empty">[docs]</a><span class="k">def</span> <span class="nf">empty</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""Returns a new array of given shape and type, without initializing entries.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> shape : int or tuple of int</span>
<span class="sd"> The shape of the empty array.</span>
<span class="sd"> ctx : Context, optional</span>
<span class="sd"> An optional device context (default is the current default context).</span>
<span class="sd"> dtype : str or numpy.dtype, optional</span>
<span class="sd"> An optional value type (default is `float32`).</span>
<span class="sd"> stype : str, optional</span>
<span class="sd"> An optional storage type (default is `default`).</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> NDArray, CSRNDArray or RowSparseNDArray</span>
<span class="sd"> A created array.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> mx.nd.empty(1)</span>
<span class="sd"> <ndarray 1="" @cpu(0)=""></ndarray></span>
<span class="sd"> >>> mx.nd.empty((1,2), mx.gpu(0))</span>
<span class="sd"> <ndarray 1x2="" @gpu(0)=""></ndarray></span>
<span class="sd"> >>> mx.nd.empty((1,2), mx.gpu(0), 'float16')</span>
<span class="sd"> <ndarray 1x2="" @gpu(0)=""></ndarray></span>
<span class="sd"> >>> mx.nd.empty((1,2), stype='csr')</span>
<span class="sd"> <csrndarray 1x2="" @cpu(0)=""></csrndarray></span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">stype</span> <span class="ow">is</span> <span class="bp">None</span> <span class="ow">or</span> <span class="n">stype</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">:</span>
<span class="k">return</span> <span class="n">_empty_ndarray</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">_empty_sparse_ndarray</span><span class="p">(</span><span class="n">stype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span></div>
<div class="viewcode-block" id="array"><a class="viewcode-back" href="../../../api/python/ndarray/ndarray.html#mxnet.ndarray.array">[docs]</a><span class="k">def</span> <span class="nf">array</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""Creates an array from any object exposing the array interface.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> source_array : array_like</span>
<span class="sd"> An object exposing the array interface, an object whose `__array__`</span>
<span class="sd"> method returns an array, or any (nested) sequence.</span>
<span class="sd"> ctx : Context, optional</span>
<span class="sd"> Device context (default is the current default context).</span>
<span class="sd"> dtype : str or numpy.dtype, optional</span>
<span class="sd"> The data type of the output array. The default dtype is ``source_array.dtype``</span>
<span class="sd"> if `source_array` is an `NDArray`, `float32` otherwise.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> NDArray, RowSparseNDArray or CSRNDArray</span>
<span class="sd"> An array with the same contents as the `source_array`.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> import numpy as np</span>
<span class="sd"> >>> mx.nd.array([1, 2, 3])</span>
<span class="sd"> <ndarray 3="" @cpu(0)=""></ndarray></span>
<span class="sd"> >>> mx.nd.array([[1, 2], [3, 4]])</span>
<span class="sd"> <ndarray 2x2="" @cpu(0)=""></ndarray></span>
<span class="sd"> >>> mx.nd.array(np.zeros((3, 2)))</span>
<span class="sd"> <ndarray 3x2="" @cpu(0)=""></ndarray></span>
<span class="sd"> >>> mx.nd.array(np.zeros((3, 2)), mx.gpu(0))</span>
<span class="sd"> <ndarray 3x2="" @gpu(0)=""></ndarray></span>
<span class="sd"> >>> mx.nd.array(mx.nd.zeros((3, 2), stype='row_sparse'))</span>
<span class="sd"> <rowsparsendarray 3x2="" @cpu(0)=""></rowsparsendarray></span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">spsp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">spsp</span><span class="o">.</span><span class="n">csr</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_sparse_array</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">source_array</span><span class="o">.</span><span class="n">stype</span> <span class="o">!=</span> <span class="s1">'default'</span><span class="p">:</span>
<span class="k">return</span> <span class="n">_sparse_array</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">_array</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span></div>
<div class="viewcode-block" id="load"><a class="viewcode-back" href="../../../api/python/ndarray/sparse.html#mxnet.ndarray.load">[docs]</a><span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="n">fname</span><span class="p">):</span>
<span class="sd">"""Loads an array from file.</span>
<span class="sd"> See more details in ``save``.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> fname : str</span>
<span class="sd"> The filename.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> list of NDArray, RowSparseNDArray or CSRNDArray, or \</span>
<span class="sd"> dict of str to NDArray, RowSparseNDArray or CSRNDArray</span>
<span class="sd"> Loaded data.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="n">string_types</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'fname required to be a string'</span><span class="p">)</span>
<span class="n">out_size</span> <span class="o">=</span> <span class="n">mx_uint</span><span class="p">()</span>
<span class="n">out_name_size</span> <span class="o">=</span> <span class="n">mx_uint</span><span class="p">()</span>
<span class="n">handles</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">)()</span>
<span class="n">names</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">)()</span>
<span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayLoad</span><span class="p">(</span><span class="n">c_str</span><span class="p">(</span><span class="n">fname</span><span class="p">),</span>
<span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">out_size</span><span class="p">),</span>
<span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">handles</span><span class="p">),</span>
<span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">out_name_size</span><span class="p">),</span>
<span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">names</span><span class="p">)))</span>
<span class="k">if</span> <span class="n">out_name_size</span><span class="o">.</span><span class="n">value</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="p">[</span><span class="n">_ndarray_cls</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">(</span><span class="n">handles</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">out_size</span><span class="o">.</span><span class="n">value</span><span class="p">)]</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">out_name_size</span><span class="o">.</span><span class="n">value</span> <span class="o">==</span> <span class="n">out_size</span><span class="o">.</span><span class="n">value</span>
<span class="k">return</span> <span class="nb">dict</span><span class="p">(</span>
<span class="p">(</span><span class="n">py_str</span><span class="p">(</span><span class="n">names</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> <span class="n">_ndarray_cls</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">(</span><span class="n">handles</span><span class="p">[</span><span class="n">i</span><span class="p">])))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">out_size</span><span class="o">.</span><span class="n">value</span><span class="p">))</span></div>
<div class="viewcode-block" id="save"><a class="viewcode-back" href="../../../api/python/ndarray/sparse.html#mxnet.ndarray.save">[docs]</a><span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
<span class="sd">"""Saves a list of arrays or a dict of str->array to file.</span>
<span class="sd"> Examples of filenames:</span>
<span class="sd"> - ``/path/to/file``</span>
<span class="sd"> - ``s3://my-bucket/path/to/file`` (if compiled with AWS S3 supports)</span>
<span class="sd"> - ``hdfs://path/to/file`` (if compiled with HDFS supports)</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> fname : str</span>
<span class="sd"> The filename.</span>
<span class="sd"> data : NDArray, RowSparseNDArray or CSRNDArray, \</span>
<span class="sd"> or list of NDArray, RowSparseNDArray or CSRNDArray, \</span>
<span class="sd"> or dict of str to NDArray, RowSparseNDArray or CSRNDArray</span>
<span class="sd"> The data to save.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> x = mx.nd.zeros((2,3))</span>
<span class="sd"> >>> y = mx.nd.ones((1,4))</span>
<span class="sd"> >>> mx.nd.save('my_list', [x,y])</span>
<span class="sd"> >>> mx.nd.save('my_dict', {'x':x, 'y':y})</span>
<span class="sd"> >>> mx.nd.load('my_list')</span>
<span class="sd"> [<ndarray 2x3="" @cpu(0)="">, <ndarray 1x4="" @cpu(0)="">]</ndarray></ndarray></span>
<span class="sd"> >>> mx.nd.load('my_dict')</span>
<span class="sd"> {'y': <ndarray 1x4="" @cpu(0)="">, 'x': <ndarray 2x3="" @cpu(0)="">}</ndarray></ndarray></span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">]</span>
<span class="n">handles</span> <span class="o">=</span> <span class="n">c_array</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">,</span> <span class="p">[])</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
<span class="n">str_keys</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
<span class="n">nd_vals</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">string_types</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">str_keys</span><span class="p">)</span> <span class="ow">or</span> \
<span class="nb">any</span><span class="p">(</span><span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">nd_vals</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'save only accept dict str->NDArray or list of NDArray'</span><span class="p">)</span>
<span class="n">keys</span> <span class="o">=</span> <span class="n">c_str_array</span><span class="p">(</span><span class="n">str_keys</span><span class="p">)</span>
<span class="n">handles</span> <span class="o">=</span> <span class="n">c_handle_array</span><span class="p">(</span><span class="n">nd_vals</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">data</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'save only accept dict str->NDArray or list of NDArray'</span><span class="p">)</span>
<span class="n">keys</span> <span class="o">=</span> <span class="bp">None</span>
<span class="n">handles</span> <span class="o">=</span> <span class="n">c_handle_array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"data needs to either be a NDArray, dict of str, NDArray pairs "</span>
<span class="s2">"or a list of NDarrays."</span><span class="p">)</span>
<span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArraySave</span><span class="p">(</span><span class="n">c_str</span><span class="p">(</span><span class="n">fname</span><span class="p">),</span>
<span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">handles</span><span class="p">)),</span>
<span class="n">handles</span><span class="p">,</span>
<span class="n">keys</span><span class="p">))</span></div>
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