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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.rtc</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="sd">&quot;&quot;&quot;Interface to runtime cuda kernel compile module.&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="kn">import</span> <span class="nn">re</span>
 <span class="kn">import</span> <span class="nn">ctypes</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="p">,</span> <span class="n">mx_uint</span><span class="p">,</span> <span class="n">c_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="p">,</span> <span class="n">check_call</span>
 <span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">c_str</span><span class="p">,</span> <span class="n">CudaModuleHandle</span><span class="p">,</span> <span class="n">CudaKernelHandle</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">,</span> <span class="n">string_types</span>
 <span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">_DTYPE_NP_TO_MX</span><span class="p">,</span> <span class="n">_DTYPE_MX_TO_NP</span><span class="p">,</span> <span class="n">NDArray</span>

 <span class="n">_DTYPE_CPP_TO_NP</span> <span class="o">=</span> <span class="p">{</span>
     <span class="s1">&#39;float&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
     <span class="s1">&#39;double&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span>
     <span class="s1">&#39;__half&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float16</span><span class="p">,</span>
     <span class="s1">&#39;uint8_t&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span>
     <span class="s1">&#39;int&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
     <span class="s1">&#39;int32_t&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
     <span class="s1">&#39;int8_t&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">,</span>
     <span class="s1">&#39;char&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">,</span>
     <span class="s1">&#39;int64_t&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">,</span>
 <span class="p">}</span>

 <div class="viewcode-block" id="CudaModule"><a class="viewcode-back" href="../../api/mxnet/rtc/index.html#mxnet.rtc.CudaModule">[docs]</a><span class="k">class</span> <span class="nc">CudaModule</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Compile and run CUDA code from Python.</span>

 <span class="sd">    In CUDA 7.5, you need to prepend your kernel definitions</span>
 <span class="sd">    with &#39;extern &quot;C&quot;&#39; to avoid name mangling::</span>

 <span class="sd">        source = r&#39;&#39;&#39;</span>
 <span class="sd">        extern &quot;C&quot; __global__ void axpy(const float *x, float *y, float alpha) {</span>
 <span class="sd">            int i = threadIdx.x + blockIdx.x * blockDim.x;</span>
 <span class="sd">            y[i] += alpha * x[i];</span>
 <span class="sd">        }</span>
 <span class="sd">        &#39;&#39;&#39;</span>
 <span class="sd">        module = mx.rtc.CudaModule(source)</span>
 <span class="sd">        func = module.get_kernel(&quot;axpy&quot;, &quot;const float *x, float *y, float alpha&quot;)</span>
 <span class="sd">        x = mx.nd.ones((10,), ctx=mx.gpu(0))</span>
 <span class="sd">        y = mx.nd.zeros((10,), ctx=mx.gpu(0))</span>
 <span class="sd">        func.launch([x, y, 3.0], mx.gpu(0), (1, 1, 1), (10, 1, 1))</span>
 <span class="sd">        print(y)</span>

 <span class="sd">    Starting from CUDA 8.0, you can instead export functions by name.</span>
 <span class="sd">    This also allows you to use templates::</span>

 <span class="sd">        source = r&#39;&#39;&#39;</span>
 <span class="sd">        template&lt;typename DType&gt;</span>
 <span class="sd">        __global__ void axpy(const DType *x, DType *y, DType alpha) {</span>
 <span class="sd">            int i = threadIdx.x + blockIdx.x * blockDim.x;</span>
 <span class="sd">            y[i] += alpha * x[i];</span>
 <span class="sd">        }</span>
 <span class="sd">        &#39;&#39;&#39;</span>
 <span class="sd">        module = mx.rtc.CudaModule(source, exports=[&#39;axpy&lt;float&gt;&#39;, &#39;axpy&lt;double&gt;&#39;])</span>
 <span class="sd">        func32 = module.get_kernel(&quot;axpy&lt;float&gt;&quot;, &quot;const float *x, float *y, float alpha&quot;)</span>
 <span class="sd">        x = mx.nd.ones((10,), dtype=&#39;float32&#39;, ctx=mx.gpu(0))</span>
 <span class="sd">        y = mx.nd.zeros((10,), dtype=&#39;float32&#39;, ctx=mx.gpu(0))</span>
 <span class="sd">        func32.launch([x, y, 3.0], mx.gpu(0), (1, 1, 1), (10, 1, 1))</span>
 <span class="sd">        print(y)</span>

 <span class="sd">        func64 = module.get_kernel(&quot;axpy&lt;double&gt;&quot;, &quot;const double *x, double *y, double alpha&quot;)</span>
 <span class="sd">        x = mx.nd.ones((10,), dtype=&#39;float64&#39;, ctx=mx.gpu(0))</span>
 <span class="sd">        y = mx.nd.zeros((10,), dtype=&#39;float64&#39;, ctx=mx.gpu(0))</span>
 <span class="sd">        func32.launch([x, y, 3.0], mx.gpu(0), (1, 1, 1), (10, 1, 1))</span>
 <span class="sd">        print(y)</span>


 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
 <span class="sd">    source : str</span>
 <span class="sd">        Complete source code.</span>
 <span class="sd">    options : tuple of str</span>
 <span class="sd">        Compiler flags. For example, use &quot;-I/usr/local/cuda/include&quot; to</span>
 <span class="sd">        add cuda headers to include path.</span>
 <span class="sd">    exports : tuple of str</span>
 <span class="sd">        Export kernel names.</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">source</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="p">(),</span> <span class="n">exports</span><span class="o">=</span><span class="p">()):</span>
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">options</span><span class="p">,</span> <span class="n">string_types</span><span class="p">):</span>
             <span class="n">options</span> <span class="o">=</span> <span class="p">(</span><span class="n">options</span><span class="p">,)</span>
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">exports</span><span class="p">,</span> <span class="n">string_types</span><span class="p">):</span>
             <span class="n">exports</span> <span class="o">=</span> <span class="p">(</span><span class="n">exports</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">CudaModuleHandle</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">MXRtcCudaModuleCreate</span><span class="p">(</span>
             <span class="n">c_str</span><span class="p">(</span><span class="n">source</span><span class="p">),</span>
             <span class="nb">len</span><span class="p">(</span><span class="n">options</span><span class="p">),</span>
             <span class="n">c_str_array</span><span class="p">(</span><span class="n">options</span><span class="p">),</span>
             <span class="nb">len</span><span class="p">(</span><span class="n">exports</span><span class="p">),</span>
             <span class="n">c_str_array</span><span class="p">(</span><span class="n">exports</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="bp">self</span><span class="o">.</span><span class="n">handle</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">MXRtcCudaModuleFree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">))</span>

 <div class="viewcode-block" id="CudaModule.get_kernel"><a class="viewcode-back" href="../../api/mxnet/rtc/index.html#mxnet.rtc.CudaModule.get_kernel">[docs]</a>    <span class="k">def</span> <span class="nf">get_kernel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">signature</span><span class="p">):</span>
         <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Get CUDA kernel from compiled module.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        name : str</span>
 <span class="sd">            String name of the kernel.</span>
 <span class="sd">        signature : str</span>
 <span class="sd">            Function signature for the kernel. For example, if a kernel is</span>
 <span class="sd">            declared as::</span>

 <span class="sd">                extern &quot;C&quot; __global__ void axpy(const float *x, double *y, int alpha)</span>

 <span class="sd">            Then its signature should be::</span>

 <span class="sd">                const float *x, double *y, int alpha</span>

 <span class="sd">            or::</span>

 <span class="sd">                const float *, double *, int</span>

 <span class="sd">            Note that `*` in signature marks an argument as array and</span>
 <span class="sd">            `const` marks an argument as constant (input) array.</span>

 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        CudaKernel</span>
 <span class="sd">            CUDA kernels that can be launched on GPUs.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">hdl</span> <span class="o">=</span> <span class="n">CudaKernelHandle</span><span class="p">()</span>
         <span class="n">is_ndarray</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">is_const</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">dtypes</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">pattern</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;&quot;&quot;^(const)?\s?([\w_]+)\s?(\*)?\s?([\w_]+)?$&quot;&quot;&quot;</span><span class="p">)</span>
         <span class="n">args</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;\s+&quot;</span><span class="p">,</span> <span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="n">signature</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>
         <span class="k">for</span> <span class="n">arg</span> <span class="ow">in</span> <span class="n">args</span><span class="p">:</span>
             <span class="n">sanitized_arg</span> <span class="o">=</span> <span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">arg</span><span class="o">.</span><span class="n">split</span><span class="p">())</span>
             <span class="n">match</span> <span class="o">=</span> <span class="n">pattern</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="n">sanitized_arg</span><span class="p">)</span>
             <span class="k">if</span> <span class="ow">not</span> <span class="n">match</span> <span class="ow">or</span> <span class="n">match</span><span class="o">.</span><span class="n">groups</span><span class="p">()[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;const&#39;</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                     <span class="s1">&#39;Invalid function prototype &quot;</span><span class="si">%s</span><span class="s1">&quot;. Must be in the &#39;</span>
                     <span class="s1">&#39;form of &quot;(const) type (*) (name)&quot;&#39;</span><span class="o">%</span><span class="n">sanitized_arg</span><span class="p">)</span>
             <span class="n">is_const</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">bool</span><span class="p">(</span><span class="n">match</span><span class="o">.</span><span class="n">groups</span><span class="p">()[</span><span class="mi">0</span><span class="p">]))</span>
             <span class="n">dtype</span> <span class="o">=</span> <span class="n">match</span><span class="o">.</span><span class="n">groups</span><span class="p">()[</span><span class="mi">1</span><span class="p">]</span>
             <span class="n">is_ndarray</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">bool</span><span class="p">(</span><span class="n">match</span><span class="o">.</span><span class="n">groups</span><span class="p">()[</span><span class="mi">2</span><span class="p">]))</span>
             <span class="k">if</span> <span class="n">dtype</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">_DTYPE_CPP_TO_NP</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                     <span class="s2">&quot;Unsupported kernel argument type </span><span class="si">%s</span><span class="s2">. Supported types are: </span><span class="si">%s</span><span class="s2">.&quot;</span><span class="o">%</span><span class="p">(</span>
                         <span class="n">sanitized_arg</span><span class="p">,</span> <span class="s1">&#39;,&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">_DTYPE_CPP_TO_NP</span><span class="o">.</span><span class="n">keys</span><span class="p">())))</span>
             <span class="n">dtypes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_DTYPE_NP_TO_MX</span><span class="p">[</span><span class="n">_DTYPE_CPP_TO_NP</span><span class="p">[</span><span class="n">dtype</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">MXRtcCudaKernelCreate</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">c_str</span><span class="p">(</span><span class="n">name</span><span class="p">),</span>
             <span class="nb">len</span><span class="p">(</span><span class="n">dtypes</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">array</span><span class="p">(</span><span class="s1">&#39;i&#39;</span><span class="p">,</span> <span class="n">is_ndarray</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">array</span><span class="p">(</span><span class="s1">&#39;i&#39;</span><span class="p">,</span> <span class="n">is_const</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">array</span><span class="p">(</span><span class="s1">&#39;i&#39;</span><span class="p">,</span> <span class="n">dtypes</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">hdl</span><span class="p">)))</span>

         <span class="k">return</span> <span class="n">CudaKernel</span><span class="p">(</span><span class="n">hdl</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">is_ndarray</span><span class="p">,</span> <span class="n">dtypes</span><span class="p">)</span></div></div>

 <div class="viewcode-block" id="CudaKernel"><a class="viewcode-back" href="../../api/mxnet/rtc/index.html#mxnet.rtc.CudaKernel">[docs]</a><span class="k">class</span> <span class="nc">CudaKernel</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Constructs CUDA kernel. Should be created by `CudaModule.get_kernel`,</span>
 <span class="sd">    not intended to be used by users.</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">name</span><span class="p">,</span> <span class="n">is_ndarray</span><span class="p">,</span> <span class="n">dtypes</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">_name</span> <span class="o">=</span> <span class="n">name</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_is_ndarray</span> <span class="o">=</span> <span class="n">is_ndarray</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_dtypes</span> <span class="o">=</span> <span class="p">[</span><span class="n">_DTYPE_MX_TO_NP</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="n">dtypes</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">MXRtcCudaKernelFree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">))</span>

 <div class="viewcode-block" id="CudaKernel.launch"><a class="viewcode-back" href="../../api/mxnet/rtc/index.html#mxnet.rtc.CudaKernel.launch">[docs]</a>    <span class="k">def</span> <span class="nf">launch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">grid_dims</span><span class="p">,</span> <span class="n">block_dims</span><span class="p">,</span> <span class="n">shared_mem</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Launch cuda kernel.</span>

 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        args : tuple of NDArray or numbers</span>
 <span class="sd">            List of arguments for kernel. NDArrays are expected for pointer</span>
 <span class="sd">            types (e.g. `float*`, `double*`) while numbers are expected for</span>
 <span class="sd">            non-pointer types (e.g. `int`, `float`).</span>
 <span class="sd">        ctx : Context</span>
 <span class="sd">            The context to launch kernel on. Must be GPU context.</span>
 <span class="sd">        grid_dims : tuple of 3 integers</span>
 <span class="sd">            Grid dimensions for CUDA kernel.</span>
 <span class="sd">        block_dims : tuple of 3 integers</span>
 <span class="sd">            Block dimensions for CUDA kernel.</span>
 <span class="sd">        shared_mem : integer, optional</span>
 <span class="sd">            Size of dynamically allocated shared memory. Defaults to 0.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="n">ctx</span><span class="o">.</span><span class="n">device_type</span> <span class="o">==</span> <span class="s1">&#39;gpu&#39;</span><span class="p">,</span> <span class="s2">&quot;Cuda kernel can only be launched on GPU&quot;</span>
         <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_dims</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;grid_dims must be a tuple of 3 integers&quot;</span>
         <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">block_dims</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;grid_dims must be a tuple of 3 integers&quot;</span>
         <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_dtypes</span><span class="p">),</span> \
             <span class="s2">&quot;CudaKernel(</span><span class="si">%s</span><span class="s2">) expects </span><span class="si">%d</span><span class="s2"> arguments but got </span><span class="si">%d</span><span class="s2">&quot;</span><span class="o">%</span><span class="p">(</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_name</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_dtypes</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">))</span>
         <span class="n">void_args</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">ref_holder</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">arg</span><span class="p">,</span> <span class="n">is_nd</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_ndarray</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dtypes</span><span class="p">)):</span>
             <span class="k">if</span> <span class="n">is_nd</span><span class="p">:</span>
                 <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">),</span> \
                     <span class="s2">&quot;The </span><span class="si">%d</span><span class="s2">-th argument is expected to be a NDArray but got </span><span class="si">%s</span><span class="s2">&quot;</span><span class="o">%</span><span class="p">(</span>
                         <span class="n">i</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">arg</span><span class="p">))</span>
                 <span class="n">void_args</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">arg</span><span class="o">.</span><span class="n">handle</span><span class="p">)</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">),</span> \
                     <span class="s2">&quot;The </span><span class="si">%d</span><span class="s2">-th argument is expected to be a number, but got </span><span class="si">%s</span><span class="s2">&quot;</span><span class="o">%</span><span class="p">(</span>
                         <span class="n">i</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">arg</span><span class="p">))</span>
                 <span class="n">ref_holder</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">arg</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">))</span>
                 <span class="n">void_args</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ref_holder</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</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="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXRtcCudaKernelCall</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">ctx</span><span class="o">.</span><span class="n">device_id</span><span class="p">,</span>
             <span class="n">c_array</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="n">void_args</span><span class="p">),</span>
             <span class="n">mx_uint</span><span class="p">(</span><span class="n">grid_dims</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">mx_uint</span><span class="p">(</span><span class="n">grid_dims</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">mx_uint</span><span class="p">(</span><span class="n">grid_dims</span><span class="p">[</span><span class="mi">2</span><span class="p">]),</span>
             <span class="n">mx_uint</span><span class="p">(</span><span class="n">block_dims</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">mx_uint</span><span class="p">(</span><span class="n">block_dims</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">mx_uint</span><span class="p">(</span><span class="n">block_dims</span><span class="p">[</span><span class="mi">2</span><span class="p">]),</span>
             <span class="n">mx_uint</span><span class="p">(</span><span class="n">shared_mem</span><span class="p">)))</span></div></div>
 </pre></div>

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