Injects a new target into a function built by make_decorator.

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Compat aliases for migration

tf.compat.v1.app.flags.tf_decorator.rewrap

tf.compat.v1.flags.tf_decorator.rewrap(
    decorator_func, previous_target, new_target
)

This function allows replacing a function wrapped by decorator_func, assuming the decorator that wraps the function is written as described below.

The decorator function must use <decorator name>.__wrapped__ instead of the wrapped function that is normally used:

Example:

Instead of this:

def simple_parametrized_wrapper(*args, *kwds): return wrapped_fn(args, **kwds)

tf_decorator.make_decorator(simple_parametrized_wrapper, wrapped_fn)

Write this:

def simple_parametrized_wrapper(*args, *kwds): return simple_parametrizedwrapper.wrapped_(args, **kwds)

tf_decorator.make_decorator(simple_parametrized_wrapper, wrapped_fn)

Note that this process modifies decorator_func.

Args
`decorator_func`	Callable returned by `wrap`.
`previous_target`	Callable that needs to be replaced.
`new_target`	Callable to replace previous_target with.

Returns
The updated decorator. If decorator_func is not a tf_decorator, new_target is returned.

TensorFlow

tf.compat / v1 / v1.flags.tf_decorator.make_decorator

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Example:

Instead of this:

Write this:

Args

Returns

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.flags.tf_decorator.make_decorator

View aliases

Example:

Instead of this:

Write this:

Args

Returns