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 <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li>
 <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
 </ul>
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 <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li>
 </ul>
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 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
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 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
 </ul>
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 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li>
 </ul>
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 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
 <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
 <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
 </ul>
 </li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/ndarray/index.html">NDArray</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li>
 <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li>
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 <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li>
 <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li>
 <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li>
 <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li>
 <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
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 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
 <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
 <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li>
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   <div class="section" id="module-mxnet.util">
 <span id="mxnet-util"></span><h1>mxnet.util<a class="headerlink" href="#module-mxnet.util" title="Permalink to this headline">¶</a></h1>
 <p>general utility functions</p>
 <p><strong>Functions</strong></p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.get_cuda_compute_capability" title="mxnet.util.get_cuda_compute_capability"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_cuda_compute_capability</span></code></a>(ctx)</p></td>
 <td><p>Returns the cuda compute capability of the input <cite>ctx</cite>.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.getenv" title="mxnet.util.getenv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getenv</span></code></a>(name)</p></td>
 <td><p>Get the setting of an environment variable from the C Runtime.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.is_np_array" title="mxnet.util.is_np_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_np_array</span></code></a>()</p></td>
 <td><p>Checks whether the NumPy-array semantics is currently turned on.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.is_np_shape" title="mxnet.util.is_np_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_np_shape</span></code></a>()</p></td>
 <td><p>Checks whether the NumPy shape semantics is currently turned on.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.np_array" title="mxnet.util.np_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np_array</span></code></a>([active])</p></td>
 <td><p>Returns an activated/deactivated NumPy-array scope to be used in ‘with’ statement and captures code that needs the NumPy-array semantics.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.np_shape" title="mxnet.util.np_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np_shape</span></code></a>([active])</p></td>
 <td><p>Returns an activated/deactivated NumPy shape scope to be used in ‘with’ statement and captures code that needs the NumPy shape semantics, i.e.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.np_ufunc_legal_option" title="mxnet.util.np_ufunc_legal_option"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np_ufunc_legal_option</span></code></a>(key, value)</p></td>
 <td><p>Checking if ufunc arguments are legal inputs</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.reset_np" title="mxnet.util.reset_np"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_np</span></code></a>()</p></td>
 <td><p>Deactivate NumPy shape and array semantics at the same time.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.set_module" title="mxnet.util.set_module"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_module</span></code></a>(module)</p></td>
 <td><p>Decorator for overriding __module__ on a function or class.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.set_np" title="mxnet.util.set_np"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_np</span></code></a>([shape, array])</p></td>
 <td><p>Setting NumPy shape and array semantics at the same time.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.set_np_shape" title="mxnet.util.set_np_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_np_shape</span></code></a>(active)</p></td>
 <td><p>Turns on/off NumPy shape semantics, in which <cite>()</cite> represents the shape of scalar tensors, and tuples with <cite>0</cite> elements, for example, <cite>(0,)</cite>, <cite>(1, 0, 2)</cite>, represent the shapes of zero-size tensors.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.setenv" title="mxnet.util.setenv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setenv</span></code></a>(name, value)</p></td>
 <td><p>Set an environment variable in the C Runtime.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.use_np" title="mxnet.util.use_np"><code class="xref py py-obj docutils literal notranslate"><span class="pre">use_np</span></code></a>(func)</p></td>
 <td><p>A convenience decorator for wrapping user provided functions and classes in the scope of both NumPy-shape and NumPy-array semantics, which means that (1) empty tuples <cite>()</cite> and tuples with zeros, such as <cite>(0, 1)</cite>, <cite>(1, 0, 2)</cite>, will be treated as scalar tensors’ shapes and zero-size tensors’ shapes in shape inference functions of operators, instead of as unknown in legacy mode; (2) ndarrays of type <cite>mxnet.numpy.ndarray</cite> should be created instead of <cite>mx.nd.NDArray</cite>.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.use_np_array" title="mxnet.util.use_np_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">use_np_array</span></code></a>(func)</p></td>
 <td><p>A decorator wrapping Gluon <cite>Block`s and all its methods, properties, and static functions with the semantics of NumPy-array, which means that where ndarrays are created, `mxnet.numpy.ndarray`s should be created, instead of legacy ndarrays of type `mx.nd.NDArray</cite>.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.use_np_shape" title="mxnet.util.use_np_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">use_np_shape</span></code></a>(func)</p></td>
 <td><p>A decorator wrapping a function or class with activated NumPy-shape semantics.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.wrap_np_binary_func" title="mxnet.util.wrap_np_binary_func"><code class="xref py py-obj docutils literal notranslate"><span class="pre">wrap_np_binary_func</span></code></a>(func)</p></td>
 <td><p>A convenience decorator for wrapping numpy-compatible binary ufuncs to provide uniform error handling.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.wrap_np_unary_func" title="mxnet.util.wrap_np_unary_func"><code class="xref py py-obj docutils literal notranslate"><span class="pre">wrap_np_unary_func</span></code></a>(func)</p></td>
 <td><p>A convenience decorator for wrapping numpy-compatible unary ufuncs to provide uniform error handling.</p></td>
 </tr>
 </tbody>
 </table>
 <dl class="function">
 <dt id="mxnet.util.get_cuda_compute_capability">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">get_cuda_compute_capability</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#get_cuda_compute_capability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.get_cuda_compute_capability" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the cuda compute capability of the input <cite>ctx</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><p><strong>ctx</strong> (<a class="reference internal" href="../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a>) – GPU context whose corresponding cuda compute capability is to be retrieved.</p>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p><strong>cuda_compute_capability</strong> – CUDA compute capability. For example, it returns 70 for CUDA arch equal to <cite>sm_70</cite>.</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p>int</p>
 </dd>
 </dl>
 <p class="rubric">References</p>
 <p><a class="reference external" href="https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549#file-cuda_check-py">https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549#file-cuda_check-py</a></p>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.getenv">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">getenv</code><span class="sig-paren">(</span><em class="sig-param">name</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#getenv"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.getenv" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the setting of an environment variable from the C Runtime.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><p><strong>name</strong> (<em>string type</em>) – The environment variable name</p>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p><strong>value</strong> – The value of the environment variable, or None if not set</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p>string</p>
 </dd>
 </dl>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.is_np_array">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">is_np_array</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#is_np_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.is_np_array" title="Permalink to this definition">¶</a></dt>
 <dd><p>Checks whether the NumPy-array semantics is currently turned on.
 This is currently used in Gluon for checking whether an array of type <cite>mxnet.numpy.ndarray</cite>
 or <cite>mx.nd.NDArray</cite> should be created. For example, at the time when a parameter
 is created in a <cite>Block</cite>, an <cite>mxnet.numpy.ndarray</cite> is created if this returns true; else
 an <cite>mx.nd.NDArray</cite> is created.</p>
 <p>Normally, users are not recommended to use this API directly unless you known exactly
 what is going on under the hood.</p>
 <p>Please note that this is designed as an infrastructure for the incoming
 MXNet-NumPy operators. Legacy operators registered in the modules
 <cite>mx.nd</cite> and <cite>mx.sym</cite> are not guaranteed to behave like their counterparts
 in NumPy within this semantics.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
 <dd class="field-odd"><p></p>
 </dd>
 <dt class="field-even">Return type</dt>
 <dd class="field-even"><p>A bool value indicating whether the NumPy-array semantics is currently on.</p>
 </dd>
 </dl>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.is_np_shape">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">is_np_shape</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#is_np_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.is_np_shape" title="Permalink to this definition">¶</a></dt>
 <dd><p>Checks whether the NumPy shape semantics is currently turned on.
 In NumPy shape semantics, <cite>()</cite> represents the shape of scalar tensors,
 and tuples with <cite>0</cite> elements, for example, <cite>(0,)</cite>, <cite>(1, 0, 2)</cite>, represent
 the shapes of zero-size tensors. This is turned off by default for keeping
 backward compatibility.</p>
 <p>In the NumPy shape semantics, <cite>-1</cite> indicates an unknown size. For example,
 <cite>(-1, 2, 2)</cite> means that the size of the first dimension is unknown. Its size
 may be inferred during shape inference.</p>
 <p>Please note that this is designed as an infrastructure for the incoming
 MXNet-NumPy operators. Legacy operators registered in the modules
 <cite>mx.nd</cite> and <cite>mx.sym</cite> are not guaranteed to behave like their counterparts
 in NumPy within this semantics.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
 <dd class="field-odd"><p></p>
 </dd>
 <dt class="field-even">Return type</dt>
 <dd class="field-even"><p>A bool value indicating whether the NumPy shape semantics is currently on.</p>
 </dd>
 </dl>
 <p class="rubric">Example</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">prev_state</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">set_np_shape</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">prev_state</span><span class="p">)</span>
 <span class="go">False</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">is_np_shape</span><span class="p">())</span>
 <span class="go">True</span>
 </pre></div>
 </div>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.np_array">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">np_array</code><span class="sig-paren">(</span><em class="sig-param">active=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#np_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.np_array" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns an activated/deactivated NumPy-array scope to be used in ‘with’ statement
 and captures code that needs the NumPy-array semantics.</p>
 <p>Currently, this is used in Gluon to enforce array creation in <cite>Block`s as type
 `mxnet.numpy.ndarray</cite>, instead of <cite>mx.nd.NDArray</cite>.</p>
 <p>It is recommended to use the decorator <cite>use_np_array</cite> to decorate the classes
 that need this semantics, instead of using this function in a <cite>with</cite> statement
 unless you know exactly what has been scoped by this semantics.</p>
 <p>Please note that this is designed as an infrastructure for the incoming
 MXNet-NumPy operators. Legacy operators registered in the modules
 <cite>mx.nd</cite> and <cite>mx.sym</cite> are not guaranteed to behave like their counterparts
 in NumPy even within this scope.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><p><strong>active</strong> (<em>bool</em>) – Indicates whether to activate NumPy-array semantics.</p>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p>A scope object for wrapping the code w/ or w/o NumPy-shape semantics.</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p>_NumpyShapeScope</p>
 </dd>
 </dl>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.np_shape">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">np_shape</code><span class="sig-paren">(</span><em class="sig-param">active=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#np_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.np_shape" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns an activated/deactivated NumPy shape scope to be used in ‘with’ statement
 and captures code that needs the NumPy shape semantics, i.e. support of scalar and
 zero-size tensors.</p>
 <p>Please note that this is designed as an infrastructure for the incoming
 MXNet-NumPy operators. Legacy operators registered in the modules
 <cite>mx.nd</cite> and <cite>mx.sym</cite> are not guaranteed to behave like their counterparts
 in NumPy even within this scope.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><p><strong>active</strong> (<em>bool</em>) – Indicates whether to activate NumPy-shape semantics.</p>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p><ul>
 <li><p><em>_NumpyShapeScope</em> – A scope object for wrapping the code w/ or w/o NumPy-shape semantics.</p></li>
 <li><p><em>Example::</em> –</p>
 <dl>
 <dt>with mx.np_shape(active=True):</dt><dd><p># A scalar tensor’s shape is <cite>()</cite>, whose <cite>ndim</cite> is <cite>0</cite>.
 scalar = mx.nd.ones(shape=())
 assert scalar.shape == ()</p>
 <p># If NumPy shape semantics is enabled, 0 in a shape means that
 # dimension contains zero elements.
 data = mx.sym.var(“data”, shape=(0, 2, 3))
 ret = mx.sym.sin(data)
 arg_shapes, out_shapes, _ = ret.infer_shape()
 assert arg_shapes[0] == (0, 2, 3)
 assert out_shapes[0] == (0, 2, 3)</p>
 <p># -1 means unknown shape dimension size in the new NumPy shape definition
 data = mx.sym.var(“data”, shape=(-1, 2, 3))
 ret = mx.sym.sin(data)
 arg_shapes, out_shapes, _ = ret.infer_shape_partial()
 assert arg_shapes[0] == (-1, 2, 3)
 assert out_shapes[0] == (-1, 2, 3)</p>
 <p># When a shape is completely unknown when NumPy shape semantics is on, it is
 # represented as <cite>None</cite> in Python.
 data = mx.sym.var(“data”)
 ret = mx.sym.sin(data)
 arg_shapes, out_shapes, _ = ret.infer_shape_partial()
 assert arg_shapes[0] is None
 assert out_shapes[0] is None</p>
 </dd>
 <dt>with mx.np_shape(active=False):</dt><dd><p># 0 means unknown shape dimension size in the legacy shape definition.
 data = mx.sym.var(“data”, shape=(0, 2, 3))
 ret = mx.sym.sin(data)
 arg_shapes, out_shapes, _ = ret.infer_shape_partial()
 assert arg_shapes[0] == (0, 2, 3)
 assert out_shapes[0] == (0, 2, 3)</p>
 <p># When a shape is completely unknown in the legacy mode (default), its ndim is
 # equal to 0 and it is represented as <cite>()</cite> in Python.
 data = mx.sym.var(“data”)
 ret = mx.sym.sin(data)
 arg_shapes, out_shapes, _ = ret.infer_shape_partial()
 assert arg_shapes[0] == ()
 assert out_shapes[0] == ()</p>
 </dd>
 </dl>
 </li>
 </ul>
 </p>
 </dd>
 </dl>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.np_ufunc_legal_option">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">np_ufunc_legal_option</code><span class="sig-paren">(</span><em class="sig-param">key</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#np_ufunc_legal_option"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.np_ufunc_legal_option" title="Permalink to this definition">¶</a></dt>
 <dd><p>Checking if ufunc arguments are legal inputs</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>key</strong> (<em>string</em>) – the key of the ufunc argument.</p></li>
 <li><p><strong>value</strong> (<em>string</em>) – the value of the ufunc argument.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p><strong>legal</strong> – Whether or not the argument is a legal one. True when the key is one of the ufunc
 arguments and value is an allowed value. False when the key is not one of the ufunc
 arugments or the value is not an allowed value even when the key is a legal one.</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p>boolean</p>
 </dd>
 </dl>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.reset_np">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">reset_np</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#reset_np"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.reset_np" title="Permalink to this definition">¶</a></dt>
 <dd><p>Deactivate NumPy shape and array semantics at the same time.</p>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.set_module">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">set_module</code><span class="sig-paren">(</span><em class="sig-param">module</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#set_module"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.set_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decorator for overriding __module__ on a function or class.</p>
 <p>Example usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@set_module</span><span class="p">(</span><span class="s1">&#39;mxnet.numpy&#39;</span><span class="p">)</span>
 <span class="k">def</span> <span class="nf">example</span><span class="p">():</span>
     <span class="k">pass</span>

 <span class="k">assert</span> <span class="n">example</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">==</span> <span class="s1">&#39;numpy&#39;</span>
 </pre></div>
 </div>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.set_np">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">set_np</code><span class="sig-paren">(</span><em class="sig-param">shape=True</em>, <em class="sig-param">array=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#set_np"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.set_np" title="Permalink to this definition">¶</a></dt>
 <dd><p>Setting NumPy shape and array semantics at the same time.
 It is required to keep NumPy shape semantics active while activating NumPy array semantics.
 Deactivating NumPy shape semantics while NumPy array semantics is still active is not allowed.
 It is highly recommended to set these two flags to <cite>True</cite> at the same time to fully enable
 NumPy-like behaviors. Please refer to the Examples section for a better understanding.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>shape</strong> (<em>bool</em>) – A boolean value indicating whether the NumPy-shape semantics should be turned on or off.
 When this flag is set to <cite>True</cite>, zero-size and zero-dim shapes are all valid shapes in
 shape inference process, instead of treated as unknown shapes in legacy mode.</p></li>
 <li><p><strong>array</strong> (<em>bool</em>) – A boolean value indicating whether the NumPy-array semantics should be turned on or off.
 When this flag is set to <cite>True</cite>, it enables Gluon code flow to use or generate <cite>mxnet.numpy.ndarray`s
 instead of `mxnet.ndarray.NDArray</cite>. For example, a <cite>Block</cite> would create parameters of type
 <cite>mxnet.numpy.ndarray</cite>.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span>
 </pre></div>
 </div>
 <p>Creating zero-dim ndarray in legacy mode would fail at shape inference.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">())</span>
 <span class="go">mxnet.base.MXNetError: Operator _ones inferring shapes failed.</span>
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
 <span class="go">mxnet.base.MXNetError: Operator _ones inferring shapes failed.</span>
 </pre></div>
 </div>
 <p>In legacy mode, Gluon layers would create parameters and outputs of type <cite>mx.nd.NDArray</cite>.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">nn</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
 <span class="go">[[0.01983214 0.07832371]</span>
 <span class="go"> [0.01983214 0.07832371]</span>
 <span class="go"> [0.01983214 0.07832371]]</span>
 <span class="go">&lt;NDArray 3x2 @cpu(0)&gt;</span>
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="p">[</span><span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">dense</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="p">()]</span>
 <span class="go">[</span>
 <span class="go">[[0.0068339  0.01299825]</span>
 <span class="go"> [0.0301265  0.04819721]]</span>
 <span class="go">&lt;NDArray 2x2 @cpu(0)&gt;,</span>
 <span class="go">[0. 0.]</span>
 <span class="go">&lt;NDArray 2 @cpu(0)&gt;]</span>
 </pre></div>
 </div>
 <p>When the <cite>shape</cite> flag is <cite>True</cite>, both shape inferences are successful.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">np</span><span class="p">,</span> <span class="n">npx</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">npx</span><span class="o">.</span><span class="n">set_np</span><span class="p">()</span>  <span class="c1"># this is required to activate NumPy-like behaviors</span>
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">())</span>
 <span class="go">array(1.)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
 <span class="go">array([], shape=(2, 0, 3))</span>
 </pre></div>
 </div>
 <p>When the <cite>array</cite> flag is <cite>True</cite>, Gluon layers would create parameters and outputs of type <cite>mx.np.ndarray</cite>.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
 <span class="go">array([[0.01983214, 0.07832371],</span>
 <span class="go">       [0.01983214, 0.07832371],</span>
 <span class="go">       [0.01983214, 0.07832371]])</span>
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="p">[</span><span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">dense</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="p">()]</span>
 <span class="go">[array([[0.0068339 , 0.01299825],</span>
 <span class="go">       [0.0301265 , 0.04819721]]), array([0., 0.])]</span>
 </pre></div>
 </div>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.set_np_shape">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">set_np_shape</code><span class="sig-paren">(</span><em class="sig-param">active</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#set_np_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.set_np_shape" title="Permalink to this definition">¶</a></dt>
 <dd><p>Turns on/off NumPy shape semantics, in which <cite>()</cite> represents the shape of scalar tensors,
 and tuples with <cite>0</cite> elements, for example, <cite>(0,)</cite>, <cite>(1, 0, 2)</cite>, represent the shapes
 of zero-size tensors. This is turned off by default for keeping backward compatibility.</p>
 <p>Please note that this is designed as an infrastructure for the incoming
 MXNet-NumPy operators. Legacy operators registered in the modules
 <cite>mx.nd</cite> and <cite>mx.sym</cite> are not guaranteed to behave like their counterparts
 in NumPy within this semantics.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><p><strong>active</strong> (<em>bool</em>) – Indicates whether to turn on/off NumPy shape semantics.</p>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p></p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p>A bool value indicating the previous state of NumPy shape semantics.</p>
 </dd>
 </dl>
 <p class="rubric">Example</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">prev_state</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">set_np_shape</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">prev_state</span><span class="p">)</span>
 <span class="go">False</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">is_np_shape</span><span class="p">())</span>
 <span class="go">True</span>
 </pre></div>
 </div>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.setenv">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">setenv</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#setenv"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.setenv" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set an environment variable in the C Runtime.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>name</strong> (<em>string type</em>) – The environment variable name</p></li>
 <li><p><strong>value</strong> (<em>string type</em>) – The desired value to set the environment value to</p></li>
 </ul>
 </dd>
 </dl>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.use_np">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">use_np</code><span class="sig-paren">(</span><em class="sig-param">func</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#use_np"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.use_np" title="Permalink to this definition">¶</a></dt>
 <dd><p>A convenience decorator for wrapping user provided functions and classes in the scope of
 both NumPy-shape and NumPy-array semantics, which means that (1) empty tuples <cite>()</cite> and tuples
 with zeros, such as <cite>(0, 1)</cite>, <cite>(1, 0, 2)</cite>, will be treated as scalar tensors’ shapes and
 zero-size tensors’ shapes in shape inference functions of operators, instead of as unknown
 in legacy mode; (2) ndarrays of type <cite>mxnet.numpy.ndarray</cite> should be created instead of
 <cite>mx.nd.NDArray</cite>.</p>
 <dl>
 <dt>Example::</dt><dd><p>import mxnet as mx
 from mxnet import gluon, np</p>
 <dl class="simple">
 <dt>class TestHybridBlock1(gluon.HybridBlock):</dt><dd><dl class="simple">
 <dt>def __init__(self):</dt><dd><p>super(TestHybridBlock1, self).__init__()
 self.w = self.params.get(‘w’, shape=(2, 2))</p>
 </dd>
 <dt>def hybrid_forward(self, F, x, w):</dt><dd><p>return F.dot(x, w) + F.ones((1,))</p>
 </dd>
 </dl>
 </dd>
 </dl>
 <p>x = mx.nd.ones((2, 2))
 net1 = TestHybridBlock1()
 net1.initialize()
 out = net1.forward(x)
 for _, v in net1.collect_params().items():</p>
 <blockquote>
 <div><p>assert type(v.data()) is mx.nd.NDArray</p>
 </div></blockquote>
 <p>assert type(out) is mx.nd.NDArray</p>
 <p>&#64;np.use_np
 class TestHybridBlock2(gluon.HybridBlock):</p>
 <blockquote>
 <div><dl class="simple">
 <dt>def __init__(self):</dt><dd><p>super(TestHybridBlock2, self).__init__()
 self.w = self.params.get(‘w’, shape=(2, 2))</p>
 </dd>
 <dt>def hybrid_forward(self, F, x, w):</dt><dd><p>return F.np.dot(x, w) + F.np.ones(())</p>
 </dd>
 </dl>
 </div></blockquote>
 <p>x = np.ones((2, 2))
 net2 = TestHybridBlock2()
 net2.initialize()
 out = net2.forward(x)
 for _, v in net2.collect_params().items():</p>
 <blockquote>
 <div><p>print(type(v.data()))
 assert type(v.data()) is np.ndarray</p>
 </div></blockquote>
 <p>assert type(out) is np.ndarray</p>
 </dd>
 </dl>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>func</strong> (<em>a user-provided callable function</em><em> or </em><em>class to be scoped by the</em>) – </p></li>
 <li><p><strong>and NumPy-array semantics.</strong> (<em>NumPy-shape</em>) – </p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p>A function or class wrapped in the Numpy-shape and NumPy-array scope.</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p>Function or class</p>
 </dd>
 </dl>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.use_np_array">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">use_np_array</code><span class="sig-paren">(</span><em class="sig-param">func</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#use_np_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.use_np_array" title="Permalink to this definition">¶</a></dt>
 <dd><p>A decorator wrapping Gluon <cite>Block`s and all its methods, properties, and static functions
 with the semantics of NumPy-array, which means that where ndarrays are created,
 `mxnet.numpy.ndarray`s should be created, instead of legacy ndarrays of type `mx.nd.NDArray</cite>.
 For example, at the time when a parameter is created in a <cite>Block</cite>, an <cite>mxnet.numpy.ndarray</cite>
 is created if it’s decorated with this decorator.</p>
 <dl>
 <dt>Example::</dt><dd><p>import mxnet as mx
 from mxnet import gluon, np</p>
 <dl class="simple">
 <dt>class TestHybridBlock1(gluon.HybridBlock):</dt><dd><dl class="simple">
 <dt>def __init__(self):</dt><dd><p>super(TestHybridBlock1, self).__init__()
 self.w = self.params.get(‘w’, shape=(2, 2))</p>
 </dd>
 <dt>def hybrid_forward(self, F, x, w):</dt><dd><p>return F.dot(x, w)</p>
 </dd>
 </dl>
 </dd>
 </dl>
 <p>x = mx.nd.ones((2, 2))
 net1 = TestHybridBlock1()
 net1.initialize()
 out = net1.forward(x)
 for _, v in net1.collect_params().items():</p>
 <blockquote>
 <div><p>assert type(v.data()) is mx.nd.NDArray</p>
 </div></blockquote>
 <p>assert type(out) is mx.nd.NDArray</p>
 <p>&#64;np.use_np_array
 class TestHybridBlock2(gluon.HybridBlock):</p>
 <blockquote>
 <div><dl class="simple">
 <dt>def __init__(self):</dt><dd><p>super(TestHybridBlock2, self).__init__()
 self.w = self.params.get(‘w’, shape=(2, 2))</p>
 </dd>
 <dt>def hybrid_forward(self, F, x, w):</dt><dd><p>return F.np.dot(x, w)</p>
 </dd>
 </dl>
 </div></blockquote>
 <p>x = np.ones((2, 2))
 net2 = TestHybridBlock2()
 net2.initialize()
 out = net2.forward(x)
 for _, v in net2.collect_params().items():</p>
 <blockquote>
 <div><p>print(type(v.data()))
 assert type(v.data()) is np.ndarray</p>
 </div></blockquote>
 <p>assert type(out) is np.ndarray</p>
 </dd>
 </dl>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><p><strong>func</strong> (<em>a user-provided callable function</em><em> or </em><em>class to be scoped by the NumPy-array semantics.</em>) – </p>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p>A function or class wrapped in the NumPy-array scope.</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p>Function or class</p>
 </dd>
 </dl>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.use_np_shape">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">use_np_shape</code><span class="sig-paren">(</span><em class="sig-param">func</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#use_np_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.use_np_shape" title="Permalink to this definition">¶</a></dt>
 <dd><p>A decorator wrapping a function or class with activated NumPy-shape semantics.
 When <cite>func</cite> is a function, this ensures that the execution of the function is scoped with NumPy
 shape semantics, such as the support for zero-dim and zero size tensors. When
 <cite>func</cite> is a class, it ensures that all the methods, static functions, and properties
 of the class are executed with the NumPy shape semantics.</p>
 <dl>
 <dt>Example::</dt><dd><p>import mxnet as mx
 &#64;mx.use_np_shape
 def scalar_one():</p>
 <blockquote>
 <div><p>return mx.nd.ones(())</p>
 </div></blockquote>
 <p>print(scalar_one())</p>
 <p>&#64;np.use_np_shape
 class ScalarTensor(object):</p>
 <blockquote>
 <div><dl>
 <dt>def __init__(self, val=None):</dt><dd><dl class="simple">
 <dt>if val is None:</dt><dd><p>val = ScalarTensor.random().value</p>
 </dd>
 </dl>
 <p>self._scalar = mx.nd.ones(()) * val</p>
 </dd>
 <dt>def __repr__(self):</dt><dd><p>print(“Is __repr__ in np_shape semantics? {}!”.format(str(np.is_np_shape())))
 return str(self._scalar.asnumpy())</p>
 </dd>
 </dl>
 <p>&#64;staticmethod
 def random():</p>
 <blockquote>
 <div><p>val = mx.nd.random.uniform().asnumpy().item()
 return ScalarTensor(val)</p>
 </div></blockquote>
 <p>&#64;property
 def value(self):</p>
 <blockquote>
 <div><p>print(“Is value property in np_shape semantics? {}!”.format(str(np.is_np_shape())))
 return self._scalar.asnumpy().item()</p>
 </div></blockquote>
 </div></blockquote>
 <p>print(“Is global scope of np_shape activated? {}!”.format(str(np.is_np_shape())))
 scalar_tensor = ScalarTensor()
 print(scalar_tensor)</p>
 </dd>
 </dl>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><p><strong>func</strong> (<em>a user-provided callable function</em><em> or </em><em>class to be scoped by the NumPy-shape semantics.</em>) – </p>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p>A function or class wrapped in the NumPy-shape scope.</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p>Function or class</p>
 </dd>
 </dl>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.wrap_np_binary_func">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">wrap_np_binary_func</code><span class="sig-paren">(</span><em class="sig-param">func</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#wrap_np_binary_func"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.wrap_np_binary_func" title="Permalink to this definition">¶</a></dt>
 <dd><p>A convenience decorator for wrapping numpy-compatible binary ufuncs to provide uniform
 error handling.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><p><strong>func</strong> (<em>a numpy-compatible binary function to be wrapped for better error handling.</em>) – </p>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p>A function wrapped with proper error handling.</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p>Function</p>
 </dd>
 </dl>
 </dd></dl>

 <dl class="function">
 <dt id="mxnet.util.wrap_np_unary_func">
 <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">wrap_np_unary_func</code><span class="sig-paren">(</span><em class="sig-param">func</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#wrap_np_unary_func"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.wrap_np_unary_func" title="Permalink to this definition">¶</a></dt>
 <dd><p>A convenience decorator for wrapping numpy-compatible unary ufuncs to provide uniform
 error handling.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><p><strong>func</strong> (<em>a numpy-compatible unary function to be wrapped for better error handling.</em>) – </p>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p>A function wrapped with proper error handling.</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p>Function</p>
 </dd>
 </dl>
 </dd></dl>

 </div>


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