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         <span class="mdl-layout-title toc">Table Of Contents</span>


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                 <ul>
 <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>
 </li>
 <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>
 </li>
 <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>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul>
 <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>
 </li>
 <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>
 </ul>
 </li>
 <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>
 </li>
 <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>
 </ul>
 </li>
 </ul>
 </li>
 <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>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/optimizer/index.html">Optimizers</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/viz/index.html">Visualization</a><ul>
 <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <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>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/mkldnn/index.html">Intel MKL-DNN</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html">Quantize with MKL-DNN backend</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html#Improving-accuracy-with-Intel®-Neural-Compressor">Improving accuracy with Intel® Neural Compressor</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/tensorrt/index.html">TensorRT</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/tensorrt/tensorrt.html">Optimizing Deep Learning Computation Graphs with TensorRT</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>
 </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>
 </li>
 <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>
 </li>
 <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>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul>
 <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>
 </li>
 <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>
 </ul>
 </li>
 <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>
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   <h1>Source code for mxnet.executor</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"># &quot;License&quot;); 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"># &quot;AS IS&quot; 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="c1"># pylint: disable=invalid-name, protected-access, too-many-locals, too-many-arguments</span>
 <span class="sd">&quot;&quot;&quot;Symbolic Executor component of MXNet.&quot;&quot;&quot;</span>

 <span class="kn">from</span> <span class="nn">array</span> <span class="kn">import</span> <span class="n">array</span> <span class="k">as</span> <span class="n">py_array</span>
 <span class="kn">import</span> <span class="nn">ctypes</span>
 <span class="kn">import</span> <span class="nn">copy</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">_LIB</span>
 <span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">mx_uint</span><span class="p">,</span> <span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">SymbolHandle</span><span class="p">,</span> <span class="n">ExecutorHandle</span><span class="p">,</span> <span class="n">py_str</span><span class="p">,</span> <span class="n">mx_int</span>
 <span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">check_call</span><span class="p">,</span> <span class="n">c_handle_array</span><span class="p">,</span> <span class="n">c_array_buf</span><span class="p">,</span> <span class="n">c_str_array</span>
 <span class="kn">from</span> <span class="nn">.</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">NDArray</span>
 <span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">_ndarray_cls</span>

 <span class="c1"># those functions are not used here, we just import them to keep backward compatibility</span>
 <span class="c1"># in case the end user calls them, as they originally lives here</span>
 <span class="c1"># pylint: disable=unused-import</span>
 <span class="kn">from</span> <span class="nn">.executor_manager</span> <span class="kn">import</span> <span class="n">_split_input_slice</span><span class="p">,</span> <span class="n">_check_arguments</span><span class="p">,</span> <span class="n">_load_data</span><span class="p">,</span> <span class="n">_load_label</span>

 <span class="k">def</span> <span class="nf">_monitor_callback_wrapper</span><span class="p">(</span><span class="n">callback</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;A wrapper for the user-defined handle.&quot;&quot;&quot;</span>
     <span class="k">def</span> <span class="nf">callback_handle</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">_</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot; ctypes function &quot;&quot;&quot;</span>
         <span class="n">callback</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">array</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">callback_handle</span>

 <div class="viewcode-block" id="Executor"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor">[docs]</a><span class="k">class</span> <span class="nc">Executor</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Executor is the object providing efficient symbolic graph execution and optimization.</span>

 <span class="sd">    Examples</span>
 <span class="sd">    --------</span>
 <span class="sd">    &gt;&gt;&gt; # typical approach to create an executor is to bind symbol</span>
 <span class="sd">    &gt;&gt;&gt; a = mx.sym.Variable(&#39;a&#39;)</span>
 <span class="sd">    &gt;&gt;&gt; b = mx.sym.Variable(&#39;b&#39;)</span>
 <span class="sd">    &gt;&gt;&gt; c = 2 * a + b</span>
 <span class="sd">    &gt;&gt;&gt; texec = c.bind(mx.cpu(), {&#39;a&#39;: mx.nd.array([1,2]), &#39;b&#39;:mx.nd.array([2,3])})</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">handle</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">grad_req</span><span class="p">,</span> <span class="n">group2ctx</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Constructor, used Symbol.bind and Symbol.simple_bind instead.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        handle: ExecutorHandle</span>
 <span class="sd">            ExecutorHandle generated by calling `bind`.</span>

 <span class="sd">        See Also</span>
 <span class="sd">        --------</span>
 <span class="sd">        Symbol.bind : to create executor.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">ExecutorHandle</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Handle type error&quot;</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">handle</span> <span class="o">=</span> <span class="n">handle</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">arg_arrays</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">grad_arrays</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">aux_arrays</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_outputs</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">symbol</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_optimized_symbol</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_arg_dict</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_grad_dict</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_aux_dict</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_output_dict</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_callback</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_all</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_ctx</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">grad_req</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_group2ctx</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">group2ctx</span><span class="p">)</span>

     <span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</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">MXExecutorFree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">))</span>

     <span class="nd">@staticmethod</span>
     <span class="k">def</span> <span class="nf">_get_dict</span><span class="p">(</span><span class="n">names</span><span class="p">,</span> <span class="n">ndarrays</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Get the dictionary given name and ndarray pairs.&quot;&quot;&quot;</span>
         <span class="n">nset</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
         <span class="k">for</span> <span class="n">nm</span> <span class="ow">in</span> <span class="n">names</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">nm</span> <span class="ow">in</span> <span class="n">nset</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Duplicate names detected, </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="n">names</span><span class="p">))</span>
             <span class="n">nset</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nm</span><span class="p">)</span>
         <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">names</span><span class="p">,</span> <span class="n">ndarrays</span><span class="p">))</span>

     <span class="k">def</span> <span class="nf">_get_outputs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;List all the output NDArray.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        A list of ndarray bound to the heads of executor.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">out_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">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXExecutorOutputs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</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">num_output</span> <span class="o">=</span> <span class="n">out_size</span><span class="o">.</span><span class="n">value</span>
         <span class="n">outputs</span> <span class="o">=</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">num_output</span><span class="p">)]</span>
         <span class="k">return</span> <span class="n">outputs</span>

 <div class="viewcode-block" id="Executor.forward"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Calculate the outputs specified by the bound symbol.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        is_train: bool, optional</span>
 <span class="sd">            Whether this forward is for evaluation purpose. If True,</span>
 <span class="sd">            a backward call is expected to follow.</span>

 <span class="sd">        **kwargs</span>
 <span class="sd">            Additional specification of input arguments.</span>

 <span class="sd">        Examples</span>
 <span class="sd">        --------</span>
 <span class="sd">        &gt;&gt;&gt; # doing forward by specifying data</span>
 <span class="sd">        &gt;&gt;&gt; texec.forward(is_train=True, data=mydata)</span>
 <span class="sd">        &gt;&gt;&gt; # doing forward by not specifying things, but copy to the executor before hand</span>
 <span class="sd">        &gt;&gt;&gt; mydata.copyto(texec.arg_dict[&#39;data&#39;])</span>
 <span class="sd">        &gt;&gt;&gt; texec.forward(is_train=True)</span>
 <span class="sd">        &gt;&gt;&gt; # doing forward by specifying data and get outputs</span>
 <span class="sd">        &gt;&gt;&gt; outputs = texec.forward(is_train=True, data=mydata)</span>
 <span class="sd">        &gt;&gt;&gt; print(outputs[0].asnumpy())</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
             <span class="n">arg_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_dict</span>
             <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                 <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="p">(</span><span class="n">NDArray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
                     <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;only accept keyword argument of NDArrays and numpy.ndarray&#39;</span><span class="p">)</span>
                 <span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">arg_dict</span><span class="p">:</span>
                     <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Unknown argument </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span>
                 <span class="k">if</span> <span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
                     <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Shape not match! Argument </span><span class="si">%s</span><span class="s1">, need: </span><span class="si">%s</span><span class="s1">, received: </span><span class="si">%s</span><span class="s1">&#39;</span>
                                      <span class="o">%</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span>
                 <span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">array</span>

         <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXExecutorForward</span><span class="p">(</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span>
             <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">is_train</span><span class="p">))))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_outputs</span><span class="p">()</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span></div>

 <div class="viewcode-block" id="Executor.backward"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor.backward">[docs]</a>    <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out_grads</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Do backward pass to get the gradient of arguments.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        out_grads : NDArray or list of NDArray or dict of str to NDArray, optional</span>
 <span class="sd">            Gradient on the outputs to be propagated back.</span>
 <span class="sd">            This parameter is only needed when bind is called</span>
 <span class="sd">            on outputs that are not a loss function.</span>
 <span class="sd">        is_train : bool, default True</span>
 <span class="sd">            Whether this backward is for training or inference. Note that in rare</span>
 <span class="sd">            cases you want to call backward with is_train=False to get gradient</span>
 <span class="sd">            during inference.</span>


 <span class="sd">        Examples</span>
 <span class="sd">        --------</span>
 <span class="sd">        &gt;&gt;&gt; # Example for binding on loss function symbol, which gives the loss value of the model.</span>
 <span class="sd">        &gt;&gt;&gt; # Equivalently it gives the head gradient for backward pass.</span>
 <span class="sd">        &gt;&gt;&gt; # In this example the built-in SoftmaxOutput is used as loss function.</span>
 <span class="sd">        &gt;&gt;&gt; # MakeLoss can be used to define customized loss function symbol.</span>
 <span class="sd">        &gt;&gt;&gt; net = mx.sym.Variable(&#39;data&#39;)</span>
 <span class="sd">        &gt;&gt;&gt; net = mx.sym.FullyConnected(net, name=&#39;fc&#39;, num_hidden=6)</span>
 <span class="sd">        &gt;&gt;&gt; net = mx.sym.Activation(net, name=&#39;relu&#39;, act_type=&quot;relu&quot;)</span>
 <span class="sd">        &gt;&gt;&gt; net = mx.sym.SoftmaxOutput(net, name=&#39;softmax&#39;)</span>

 <span class="sd">        &gt;&gt;&gt; args =  {&#39;data&#39;: mx.nd.ones((1, 4)), &#39;fc_weight&#39;: mx.nd.ones((6, 4)),</span>
 <span class="sd">        &gt;&gt;&gt;          &#39;fc_bias&#39;: mx.nd.array((1, 4, 4, 4, 5, 6)), &#39;softmax_label&#39;: mx.nd.ones((1))}</span>
 <span class="sd">        &gt;&gt;&gt; args_grad = {&#39;fc_weight&#39;: mx.nd.zeros((6, 4)), &#39;fc_bias&#39;: mx.nd.zeros((6))}</span>
 <span class="sd">        &gt;&gt;&gt; texec = net.bind(ctx=mx.cpu(), args=args, args_grad=args_grad)</span>
 <span class="sd">        &gt;&gt;&gt; out = texec.forward(is_train=True)[0].copy()</span>
 <span class="sd">        &gt;&gt;&gt; print out.asnumpy()</span>
 <span class="sd">        [[ 0.00378404  0.07600445  0.07600445  0.07600445  0.20660152  0.5616011 ]]</span>
 <span class="sd">        &gt;&gt;&gt; texec.backward()</span>
 <span class="sd">        &gt;&gt;&gt; print(texec.grad_arrays[1].asnumpy())</span>
 <span class="sd">        [[ 0.00378404  0.00378404  0.00378404  0.00378404]</span>
 <span class="sd">         [-0.92399555 -0.92399555 -0.92399555 -0.92399555]</span>
 <span class="sd">         [ 0.07600445  0.07600445  0.07600445  0.07600445]</span>
 <span class="sd">         [ 0.07600445  0.07600445  0.07600445  0.07600445]</span>
 <span class="sd">         [ 0.20660152  0.20660152  0.20660152  0.20660152]</span>
 <span class="sd">         [ 0.5616011   0.5616011   0.5616011   0.5616011 ]]</span>
 <span class="sd">        &gt;&gt;&gt;</span>
 <span class="sd">        &gt;&gt;&gt; # Example for binding on non-loss function symbol.</span>
 <span class="sd">        &gt;&gt;&gt; # Here the binding symbol is neither built-in loss function</span>
 <span class="sd">        &gt;&gt;&gt; # nor customized loss created by MakeLoss.</span>
 <span class="sd">        &gt;&gt;&gt; # As a result the head gradient is not automatically provided.</span>
 <span class="sd">        &gt;&gt;&gt; a = mx.sym.Variable(&#39;a&#39;)</span>
 <span class="sd">        &gt;&gt;&gt; b = mx.sym.Variable(&#39;b&#39;)</span>
 <span class="sd">        &gt;&gt;&gt; # c is not a loss function symbol</span>
 <span class="sd">        &gt;&gt;&gt; c = 2 * a + b</span>
 <span class="sd">        &gt;&gt;&gt; args = {&#39;a&#39;: mx.nd.array([1,2]), &#39;b&#39;:mx.nd.array([2,3])}</span>
 <span class="sd">        &gt;&gt;&gt; args_grad = {&#39;a&#39;: mx.nd.zeros((2)), &#39;b&#39;: mx.nd.zeros((2))}</span>
 <span class="sd">        &gt;&gt;&gt; texec = c.bind(ctx=mx.cpu(), args=args, args_grad=args_grad)</span>
 <span class="sd">        &gt;&gt;&gt; out = texec.forward(is_train=True)[0].copy()</span>
 <span class="sd">        &gt;&gt;&gt; print(out.asnumpy())</span>
 <span class="sd">        [ 4.  7.]</span>
 <span class="sd">        &gt;&gt;&gt; # out_grads is the head gradient in backward pass.</span>
 <span class="sd">        &gt;&gt;&gt; # Here we define &#39;c&#39; as loss function.</span>
 <span class="sd">        &gt;&gt;&gt; # Then &#39;out&#39; is passed as head gradient of backward pass.</span>
 <span class="sd">        &gt;&gt;&gt; texec.backward(out)</span>
 <span class="sd">        &gt;&gt;&gt; print(texec.grad_arrays[0].asnumpy())</span>
 <span class="sd">        [ 8.  14.]</span>
 <span class="sd">        &gt;&gt;&gt; print(texec.grad_arrays[1].asnumpy())</span>
 <span class="sd">        [ 4.  7.]</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="n">out_grads</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">out_grads</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">out_grads</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span>
             <span class="n">out_grads</span> <span class="o">=</span> <span class="p">[</span><span class="n">out_grads</span><span class="p">]</span>
         <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">out_grads</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
             <span class="n">out_grads</span> <span class="o">=</span> <span class="p">[</span><span class="n">out_grads</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()]</span>

         <span class="k">for</span> <span class="n">obj</span> <span class="ow">in</span> <span class="n">out_grads</span><span class="p">:</span>
             <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span>
                 <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;inputs must be NDArray&quot;</span><span class="p">)</span>
         <span class="n">handle_array</span> <span class="o">=</span> <span class="n">c_handle_array</span><span class="p">(</span><span class="n">out_grads</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">MXExecutorBackwardEx</span><span class="p">(</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">handle</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">out_grads</span><span class="p">)),</span>
             <span class="n">handle_array</span><span class="p">,</span>
             <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">is_train</span><span class="p">)))</span></div>

 <div class="viewcode-block" id="Executor.set_monitor_callback"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor.set_monitor_callback">[docs]</a>    <span class="k">def</span> <span class="nf">set_monitor_callback</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callback</span><span class="p">,</span> <span class="n">monitor_all</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Install callback for monitor.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        callback : function</span>
 <span class="sd">            Takes a string and an NDArrayHandle.</span>
 <span class="sd">        monitor_all : bool, default False</span>
 <span class="sd">            If true, monitor both input and output, otherwise monitor output only.</span>

 <span class="sd">        Examples</span>
 <span class="sd">        --------</span>
 <span class="sd">        &gt;&gt;&gt; def mon_callback(*args, **kwargs):</span>
 <span class="sd">        &gt;&gt;&gt;     print(&quot;Do your stuff here.&quot;)</span>
 <span class="sd">        &gt;&gt;&gt;</span>
 <span class="sd">        &gt;&gt;&gt; texe.set_monitor_callback(mon_callback)</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">cb_type</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">CFUNCTYPE</span><span class="p">(</span><span class="kc">None</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">NDArrayHandle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_callback</span> <span class="o">=</span> <span class="n">cb_type</span><span class="p">(</span><span class="n">_monitor_callback_wrapper</span><span class="p">(</span><span class="n">callback</span><span class="p">))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_all</span> <span class="o">=</span> <span class="n">monitor_all</span>
         <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXExecutorSetMonitorCallbackEX</span><span class="p">(</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_callback</span><span class="p">,</span>
             <span class="kc">None</span><span class="p">,</span>
             <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">monitor_all</span><span class="p">)))</span></div>

     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">arg_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Get dictionary representation of argument arrrays.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        arg_dict : dict of str to NDArray</span>
 <span class="sd">            The dictionary that maps the names of arguments to NDArrays.</span>

 <span class="sd">        Raises</span>
 <span class="sd">        ------</span>
 <span class="sd">        ValueError : if there are duplicated names in the arguments.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_arg_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_arg_dict</span> <span class="o">=</span> <span class="n">Executor</span><span class="o">.</span><span class="n">_get_dict</span><span class="p">(</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_arrays</span><span class="p">)</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_arg_dict</span>

     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">grad_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Get dictionary representation of gradient arrays.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        grad_dict : dict of str to NDArray</span>
 <span class="sd">            The dictionary that maps name of arguments to gradient arrays.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_grad_dict</span> <span class="o">=</span> <span class="n">Executor</span><span class="o">.</span><span class="n">_get_dict</span><span class="p">(</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">)</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_dict</span>

     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">aux_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Get dictionary representation of auxiliary states arrays.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        aux_dict : dict of str to NDArray</span>
 <span class="sd">            The dictionary that maps name of auxiliary states to NDArrays.</span>

 <span class="sd">        Raises</span>
 <span class="sd">        ------</span>
 <span class="sd">        ValueError : if there are duplicated names in the auxiliary states.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_aux_dict</span> <span class="o">=</span> <span class="n">Executor</span><span class="o">.</span><span class="n">_get_dict</span><span class="p">(</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_arrays</span><span class="p">)</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_dict</span>

     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">output_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Get dictionary representation of output arrays.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        output_dict : dict of str to NDArray</span>
 <span class="sd">            The dictionary that maps name of output names to NDArrays.</span>

 <span class="sd">        Raises</span>
 <span class="sd">        ------</span>
 <span class="sd">        ValueError : if there are duplicated names in the outputs.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_output_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_output_dict</span> <span class="o">=</span> <span class="n">Executor</span><span class="o">.</span><span class="n">_get_dict</span><span class="p">(</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span><span class="p">)</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_output_dict</span>

 <div class="viewcode-block" id="Executor.copy_params_from"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor.copy_params_from">[docs]</a>    <span class="k">def</span> <span class="nf">copy_params_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allow_extra_params</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Copy parameters from arg_params, aux_params into executor&#39;s internal array.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        arg_params : dict of str to NDArray</span>
 <span class="sd">            Parameters, dict of name to NDArray of arguments.</span>

 <span class="sd">        aux_params : dict of str to NDArray, optional</span>
 <span class="sd">            Parameters, dict of name to NDArray of auxiliary states.</span>

 <span class="sd">        allow_extra_params : boolean, optional</span>
 <span class="sd">            Whether allow extra parameters that are not needed by symbol.</span>
 <span class="sd">            If this is True, no error will be thrown when arg_params or aux_params</span>
 <span class="sd">            contain extra parameters that is not needed by the executor.</span>

 <span class="sd">        Raises</span>
 <span class="sd">        ------</span>
 <span class="sd">        ValueError</span>
 <span class="sd">            If there is additional parameters in the dict but ``allow_extra_params=False``.</span>

 <span class="sd">        Examples</span>
 <span class="sd">        --------</span>
 <span class="sd">        &gt;&gt;&gt; # set parameters with existing model checkpoint</span>
 <span class="sd">        &gt;&gt;&gt; model_prefix = &#39;mx_mlp&#39;</span>
 <span class="sd">        &gt;&gt;&gt; sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, 0)</span>
 <span class="sd">        &gt;&gt;&gt; texec.copy_params_from(arg_params, aux_params)</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">arg_params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
             <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">:</span>
                 <span class="n">dst</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
                 <span class="k">if</span> <span class="n">dst</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">([(</span><span class="s1">&#39;bfloat16&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">uint16</span><span class="p">)]):</span>
                     <span class="n">cast_array</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">amp_cast</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dst</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
                     <span class="n">cast_array</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">dst</span><span class="p">)</span>
                 <span class="k">else</span><span class="p">:</span>
                     <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dst</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">dst</span><span class="p">)</span>
             <span class="k">elif</span> <span class="ow">not</span> <span class="n">allow_extra_params</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Find name </span><span class="se">\&quot;</span><span class="si">%s</span><span class="se">\&quot;</span><span class="s1"> that is not in the arguments&#39;</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span>

         <span class="k">if</span> <span class="n">aux_params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">return</span>

         <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">aux_params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
             <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_dict</span><span class="p">:</span>
                 <span class="n">dst</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
                 <span class="k">if</span> <span class="n">dst</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">([(</span><span class="s1">&#39;bfloat16&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">uint16</span><span class="p">)]):</span>
                     <span class="n">cast_array</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">amp_cast</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dst</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
                     <span class="n">cast_array</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">dst</span><span class="p">)</span>
                 <span class="k">else</span><span class="p">:</span>
                     <span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dst</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">dst</span><span class="p">)</span>
             <span class="k">elif</span> <span class="ow">not</span> <span class="n">allow_extra_params</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Find name </span><span class="si">%s</span><span class="s1"> that is not in the auxiliary states&#39;</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span></div>

 <div class="viewcode-block" id="Executor.reshape"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor.reshape">[docs]</a>    <span class="k">def</span> <span class="nf">reshape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">partial_shaping</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">allow_up_sizing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Return a new executor with the same symbol and shared memory,</span>
 <span class="sd">        but different input/output shapes.</span>
 <span class="sd">        For runtime reshaping, variable length sequences, etc.</span>
 <span class="sd">        The returned executor shares state with the current one,</span>
 <span class="sd">        and cannot be used in parallel with it.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        partial_shaping : bool</span>
 <span class="sd">            Whether to allow changing the shape of unspecified arguments.</span>
 <span class="sd">        allow_up_sizing : bool</span>
 <span class="sd">            Whether to allow allocating new ndarrays that&#39;s larger than the original.</span>
 <span class="sd">        kwargs : dict of string to tuple of int</span>
 <span class="sd">            New shape for arguments.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        exec : Executor</span>
 <span class="sd">            A new executor that shares memory with self.</span>

 <span class="sd">        Examples</span>
 <span class="sd">        --------</span>
 <span class="sd">        &gt;&gt;&gt; a = mx.sym.Variable(&#39;a&#39;)</span>
 <span class="sd">        &gt;&gt;&gt; b = mx.sym.Variable(&#39;b&#39;)</span>
 <span class="sd">        &gt;&gt;&gt; c = 2 * a + b</span>
 <span class="sd">        &gt;&gt;&gt; texec = c.bind(mx.cpu(), {&#39;a&#39;: mx.nd.zeros((2, 1)), &#39;b&#39;: mx.nd.ones((2,1))})</span>
 <span class="sd">        &gt;&gt;&gt; new_shape = {&#39;a&#39;: (4, 2), &#39;b&#39;: (4, 2)}</span>
 <span class="sd">        &gt;&gt;&gt; texec.reshape(allow_up_sizing=True, **new_shape)</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="c1"># pylint: disable=too-many-branches</span>
         <span class="n">provided_arg_shape_data</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># shape data</span>
         <span class="c1"># argument shape index in sdata,</span>
         <span class="c1"># e.g. [sdata[indptr[0]], sdata[indptr[1]]) is the shape of the first arg</span>
         <span class="n">provided_arg_shape_idx</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="n">provided_arg_shape_names</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># provided argument names</span>
         <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
             <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
                 <span class="n">provided_arg_shape_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
                 <span class="n">provided_arg_shape_data</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
                 <span class="n">provided_arg_shape_idx</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">provided_arg_shape_data</span><span class="p">))</span>

         <span class="n">ctx_map_keys</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">ctx_map_dev_types</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">ctx_map_dev_ids</span> <span class="o">=</span> <span class="p">[]</span>

         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_group2ctx</span><span class="p">:</span>
             <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_group2ctx</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                 <span class="n">ctx_map_keys</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
                 <span class="n">ctx_map_dev_types</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">device_typeid</span><span class="p">)</span>
                 <span class="n">ctx_map_dev_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">device_id</span><span class="p">)</span>

         <span class="n">handle</span> <span class="o">=</span> <span class="n">ExecutorHandle</span><span class="p">()</span>
         <span class="n">shared_handle</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">handle</span>

         <span class="n">num_in_args</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint</span><span class="p">()</span>
         <span class="n">in_arg_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">arg_grad_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">num_aux_states</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint</span><span class="p">()</span>
         <span class="n">aux_state_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">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXExecutorReshapeEx</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">partial_shaping</span><span class="p">)),</span>
                                             <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">allow_up_sizing</span><span class="p">)),</span>
                                             <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_ctx</span><span class="o">.</span><span class="n">device_typeid</span><span class="p">),</span>
                                             <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_ctx</span><span class="o">.</span><span class="n">device_id</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">ctx_map_keys</span><span class="p">)),</span>
                                             <span class="n">c_str_array</span><span class="p">(</span><span class="n">ctx_map_keys</span><span class="p">),</span>
                                             <span class="n">c_array_buf</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span>
                                                         <span class="n">py_array</span><span class="p">(</span><span class="s1">&#39;i&#39;</span><span class="p">,</span> <span class="n">ctx_map_dev_types</span><span class="p">)),</span>
                                             <span class="n">c_array_buf</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span>
                                                         <span class="n">py_array</span><span class="p">(</span><span class="s1">&#39;i&#39;</span><span class="p">,</span> <span class="n">ctx_map_dev_ids</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">provided_arg_shape_names</span><span class="p">)),</span>
                                             <span class="n">c_str_array</span><span class="p">(</span><span class="n">provided_arg_shape_names</span><span class="p">),</span>
                                             <span class="n">c_array_buf</span><span class="p">(</span><span class="n">mx_int</span><span class="p">,</span>
                                                         <span class="n">py_array</span><span class="p">(</span><span class="s1">&#39;i&#39;</span><span class="p">,</span> <span class="n">provided_arg_shape_data</span><span class="p">)),</span>
                                             <span class="n">c_array_buf</span><span class="p">(</span><span class="n">mx_uint</span><span class="p">,</span>
                                                         <span class="n">py_array</span><span class="p">(</span><span class="s1">&#39;I&#39;</span><span class="p">,</span> <span class="n">provided_arg_shape_idx</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">num_in_args</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">in_arg_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">arg_grad_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">num_aux_states</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">aux_state_handles</span><span class="p">),</span>
                                             <span class="n">shared_handle</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">handle</span><span class="p">)))</span>

         <span class="n">arg_arrays</span> <span class="o">=</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">in_arg_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">num_in_args</span><span class="o">.</span><span class="n">value</span><span class="p">)]</span>
         <span class="n">grad_arrays</span> <span class="o">=</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">arg_grad_handles</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
                        <span class="k">if</span> <span class="n">arg_grad_handles</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
                        <span class="k">else</span> <span class="kc">None</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">num_in_args</span><span class="o">.</span><span class="n">value</span><span class="p">)]</span>
         <span class="n">aux_arrays</span> <span class="o">=</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">aux_state_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">num_aux_states</span><span class="o">.</span><span class="n">value</span><span class="p">)]</span>

         <span class="n">executor</span> <span class="o">=</span> <span class="n">Executor</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ctx</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_group2ctx</span><span class="p">)</span>
         <span class="n">executor</span><span class="o">.</span><span class="n">arg_arrays</span> <span class="o">=</span> <span class="n">arg_arrays</span>
         <span class="n">executor</span><span class="o">.</span><span class="n">grad_arrays</span> <span class="o">=</span> <span class="n">grad_arrays</span>
         <span class="n">executor</span><span class="o">.</span><span class="n">aux_arrays</span> <span class="o">=</span> <span class="n">aux_arrays</span>
         <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_monitor_callback</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_monitor_all</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">):</span>
             <span class="c1"># rebind callback to the new executor if the callback is valid</span>
             <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXExecutorSetMonitorCallbackEX</span><span class="p">(</span>
                 <span class="n">handle</span><span class="p">,</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_callback</span><span class="p">,</span>
                 <span class="kc">None</span><span class="p">,</span>
                 <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_monitor_all</span><span class="p">)))</span>
         <span class="k">return</span> <span class="n">executor</span></div>

 <div class="viewcode-block" id="Executor.debug_str"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor.debug_str">[docs]</a>    <span class="k">def</span> <span class="nf">debug_str</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Get a debug string about internal execution plan.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        debug_str : string</span>
 <span class="sd">            Debug string of the executor.</span>

 <span class="sd">        Examples</span>
 <span class="sd">        --------</span>
 <span class="sd">        &gt;&gt;&gt; a = mx.sym.Variable(&#39;a&#39;)</span>
 <span class="sd">        &gt;&gt;&gt; b = mx.sym.sin(a)</span>
 <span class="sd">        &gt;&gt;&gt; c = 2 * a + b</span>
 <span class="sd">        &gt;&gt;&gt; texec = c.bind(mx.cpu(), {&#39;a&#39;: mx.nd.array([1,2]), &#39;b&#39;:mx.nd.array([2,3])})</span>
 <span class="sd">        &gt;&gt;&gt; print(texec.debug_str())</span>
 <span class="sd">        Symbol Outputs:</span>
 <span class="sd">	            output[0]=_plus0(0)</span>
 <span class="sd">        Variable:a</span>
 <span class="sd">        --------------------</span>
 <span class="sd">        Op:_mul_scalar, Name=_mulscalar0</span>
 <span class="sd">        Inputs:</span>
 <span class="sd">	        arg[0]=a(0) version=0</span>
 <span class="sd">        Attrs:</span>
 <span class="sd">	        scalar=2</span>
 <span class="sd">        --------------------</span>
 <span class="sd">        Op:sin, Name=sin0</span>
 <span class="sd">        Inputs:</span>
 <span class="sd">	        arg[0]=a(0) version=0</span>
 <span class="sd">        --------------------</span>
 <span class="sd">        Op:elemwise_add, Name=_plus0</span>
 <span class="sd">        Inputs:</span>
 <span class="sd">	        arg[0]=_mulscalar0(0)</span>
 <span class="sd">	        arg[1]=sin0(0)</span>
 <span class="sd">        Total 0 MB allocated</span>
 <span class="sd">        Total 11 TempSpace resource requested</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">debug_str</span> <span class="o">=</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">MXExecutorPrint</span><span class="p">(</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">handle</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">debug_str</span><span class="p">)))</span>
         <span class="k">return</span> <span class="n">py_str</span><span class="p">(</span><span class="n">debug_str</span><span class="o">.</span><span class="n">value</span><span class="p">)</span></div>

 <div class="viewcode-block" id="Executor.get_optimized_symbol"><a class="viewcode-back" href="../../api/mxnet/executor/index.html#mxnet.executor.Executor.get_optimized_symbol">[docs]</a>    <span class="k">def</span> <span class="nf">get_optimized_symbol</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Get an optimized version of the symbol from the executor.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        symbol : Symbol</span>
 <span class="sd">            Optimized symbol from the executor.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="kn">from</span> <span class="nn">.symbol</span> <span class="kn">import</span> <span class="n">Symbol</span>
         <span class="n">sym_handle</span> <span class="o">=</span> <span class="n">SymbolHandle</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">MXExecutorGetOptimizedSymbol</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</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">sym_handle</span><span class="p">)))</span>
         <span class="n">ret</span> <span class="o">=</span> <span class="n">Symbol</span><span class="p">(</span><span class="n">sym_handle</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">ret</span></div></div>
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