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Builds an operator that compiles and runs `computation`

with XLA.

tf.xla.experimental.compile( computation, inputs=None )

Note:In eager mode,`computation`

will have`@tf.function`

semantics.

Args | |
---|---|

`computation` | A Python function that builds a computation to apply to the input. If the function takes n inputs, 'inputs' should be a list of n tensors.
All |

`inputs` | A list of inputs or `None` (equivalent to an empty list). Each input can be a nested structure containing values that are convertible to tensors. Note that passing an N-dimension list of compatible values will result in a N-dimension list of scalar tensors rather than a single Rank-N tensors. If you need different behavior, convert part of inputs to tensors with `tf.convert_to_tensor` . |

Returns | |
---|---|

Same data structure as if computation(*inputs) is called directly with some exceptions for correctness. Exceptions include: 1) None output: a NoOp would be returned which control-depends on computation. 2) Single value output: A tuple containing the value would be returned. 3) Operation-only outputs: a NoOp would be returned which control-depends on computation. |

Raises | |
---|---|

`RuntimeError` | if called when eager execution is enabled. |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/xla/experimental/compile