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        "author": "Andreas @blackhc Kirsch",
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            "Intended Audience :: Developers",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# TfPyTh\n\n[![Build Status](https://travis-ci.com/BlackHC/tfpyth.svg?branch=master)](https://travis-ci.com/BlackHC/tfpyth) [![codecov](https://codecov.io/gh/BlackHC/tfpyth/branch/master/graph/badge.svg)](https://codecov.io/gh/BlackHC/tfpyth)\n\nPutting TensorFlow back in PyTorch, back in TensorFlow (differentiable TensorFlow PyTorch adapters).\n\n> A light-weight differentiable adapter library to make TensorFlow and PyTorch interact.\n\n## Install\n\n```\npip install tfpyth\n```\n\n### Example\n\n```python\nimport tensorflow as tf\nimport torch as th\nimport numpy as np\nimport tfpyth\n\nsession = tf.Session()\n\ndef get_torch_function():\n    a = tf.placeholder(tf.float32, name='a')\n    b = tf.placeholder(tf.float32, name='b')\n    c = 3 * a + 4 * b * b\n\n    f = tfpyth.torch_from_tensorflow(session, [a, b], c).apply\n    return f\n\nf = get_torch_function()\na = th.tensor(1, dtype=th.float32, requires_grad=True)\nb = th.tensor(3, dtype=th.float32, requires_grad=True)\nx = f(a, b)\n\nassert x == 39.\n\nx.backward()\n\nassert np.allclose((a.grad, b.grad), (3., 24.))\n```\n\n## What it's got\n\n### `torch_from_tensorflow`\n\nCreates a PyTorch function that is differentiable by evaluating a TensorFlow output tensor given input placeholders.\n\n### `eager_tensorflow_from_torch`\n\nCreates an eager Tensorflow function from a PyTorch function.\n\n### `tensorflow_from_torch`\n\nCreates a TensorFlow op/tensor from a PyTorch function.\n\n## Future work\n\n- [ ] support JAX\n- [ ] support higher-order derivatives\n\n\n",
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