"""Python wrappers around TensorFlow ops.
This file is MACHINE GENERATED! Do not edit.
Original C++ source file: random_index_shuffle_ops.cc
"""
import collections
from tensorflow.python import pywrap_tfe as pywrap_tfe
from tensorflow.python.eager import context as _context
from tensorflow.python.eager import core as _core
from tensorflow.python.eager import execute as _execute
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import op_def_registry as _op_def_registry
from tensorflow.python.framework import ops as _ops
from tensorflow.python.framework import op_def_library as _op_def_library
from tensorflow.python.util.deprecation import deprecated_endpoints
from tensorflow.python.util import dispatch as _dispatch
from tensorflow.python.util.tf_export import tf_export
from typing import TypeVar
[文档]@_dispatch.add_fallback_dispatch_list
@_dispatch.add_type_based_api_dispatcher
@tf_export('random_index_shuffle')
def random_index_shuffle(index, seed, max_index, name=None):
r"""Outputs the position of `value` in a permutation of [0, ..., max_index].
Output values are a bijection of the `index` for any combination and `seed` and `max_index`.
If multiple inputs are vectors (matrix in case of seed) then the size of the
first dimension must match.
The outputs are deterministic.
Args:
index: A `Tensor`. Must be one of the following types: `int32`, `uint32`, `int64`, `uint64`.
A scalar tensor or a vector of dtype `dtype`. The index (or indices) to be shuffled. Must be within [0, max_index].
seed: A `Tensor`. Must be one of the following types: `int32`, `uint32`, `int64`, `uint64`.
A tensor of dtype `Tseed` and shape [3] or [n, 3]. The random seed.
max_index: A `Tensor`. Must have the same type as `index`.
A scalar tensor or vector of dtype `dtype`. The upper bound(s) of the interval (inclusive).
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `index`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "RandomIndexShuffle", name, index, seed, max_index)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
_result = _dispatcher_for_random_index_shuffle(
(index, seed, max_index, name,), None)
if _result is not NotImplemented:
return _result
return random_index_shuffle_eager_fallback(
index, seed, max_index, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
_result = _dispatch.dispatch(
random_index_shuffle, (), dict(index=index, seed=seed,
max_index=max_index, name=name)
)
if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return _result
raise
else:
_result = _dispatcher_for_random_index_shuffle(
(index, seed, max_index, name,), None)
if _result is not NotImplemented:
return _result
# Add nodes to the TensorFlow graph.
try:
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"RandomIndexShuffle", index=index, seed=seed, max_index=max_index,
name=name)
except (TypeError, ValueError):
_result = _dispatch.dispatch(
random_index_shuffle, (), dict(index=index, seed=seed,
max_index=max_index, name=name)
)
if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return _result
raise
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("dtype", _op._get_attr_type("dtype"), "Tseed",
_op._get_attr_type("Tseed"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"RandomIndexShuffle", _inputs_flat, _attrs, _result)
_result, = _result
return _result
RandomIndexShuffle = tf_export("raw_ops.RandomIndexShuffle")(_ops.to_raw_op(random_index_shuffle))
_dispatcher_for_random_index_shuffle = random_index_shuffle._tf_type_based_dispatcher.Dispatch
def random_index_shuffle_eager_fallback(index, seed, max_index, name, ctx):
_attr_dtype, _inputs_dtype = _execute.args_to_matching_eager([index, max_index], ctx, [_dtypes.int32, _dtypes.uint32, _dtypes.int64, _dtypes.uint64, ])
(index, max_index) = _inputs_dtype
_attr_Tseed, (seed,) = _execute.args_to_matching_eager([seed], ctx, [_dtypes.int32, _dtypes.uint32, _dtypes.int64, _dtypes.uint64, ])
_inputs_flat = [index, seed, max_index]
_attrs = ("dtype", _attr_dtype, "Tseed", _attr_Tseed)
_result = _execute.execute(b"RandomIndexShuffle", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"RandomIndexShuffle", _inputs_flat, _attrs, _result)
_result, = _result
return _result