261 lines
9.3 KiB
Python
261 lines
9.3 KiB
Python
|
"""Python wrappers around TensorFlow ops.
|
||
|
|
|
||
|
|
This file is MACHINE GENERATED! Do not edit.
|
||
|
|
"""
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
def optional_from_value(components, name=None):
|
||
|
|
r"""Constructs an Optional variant from a tuple of tensors.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
components: A list of `Tensor` objects.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `variant`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "OptionalFromValue", name, components)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return optional_from_value_eager_fallback(
|
||
|
|
components, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"OptionalFromValue", components=components, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("Toutput_types", _op.get_attr("Toutput_types"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"OptionalFromValue", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
OptionalFromValue = tf_export("raw_ops.OptionalFromValue")(_ops.to_raw_op(optional_from_value))
|
||
|
|
|
||
|
|
|
||
|
|
def optional_from_value_eager_fallback(components, name, ctx):
|
||
|
|
_attr_Toutput_types, components = _execute.convert_to_mixed_eager_tensors(components, ctx)
|
||
|
|
_inputs_flat = list(components)
|
||
|
|
_attrs = ("Toutput_types", _attr_Toutput_types)
|
||
|
|
_result = _execute.execute(b"OptionalFromValue", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"OptionalFromValue", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def optional_get_value(optional, output_types, output_shapes, name=None):
|
||
|
|
r"""Returns the value stored in an Optional variant or raises an error if none exists.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
optional: A `Tensor` of type `variant`.
|
||
|
|
output_types: A list of `tf.DTypes` that has length `>= 1`.
|
||
|
|
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A list of `Tensor` objects of type `output_types`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "OptionalGetValue", name, optional, "output_types",
|
||
|
|
output_types, "output_shapes", output_shapes)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return optional_get_value_eager_fallback(
|
||
|
|
optional, output_types=output_types, output_shapes=output_shapes,
|
||
|
|
name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
if not isinstance(output_types, (list, tuple)):
|
||
|
|
raise TypeError(
|
||
|
|
"Expected list for 'output_types' argument to "
|
||
|
|
"'optional_get_value' Op, not %r." % output_types)
|
||
|
|
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
|
||
|
|
if not isinstance(output_shapes, (list, tuple)):
|
||
|
|
raise TypeError(
|
||
|
|
"Expected list for 'output_shapes' argument to "
|
||
|
|
"'optional_get_value' Op, not %r." % output_shapes)
|
||
|
|
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"OptionalGetValue", optional=optional, output_types=output_types,
|
||
|
|
output_shapes=output_shapes, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
|
||
|
|
_op.get_attr("output_shapes"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"OptionalGetValue", _inputs_flat, _attrs, _result)
|
||
|
|
return _result
|
||
|
|
|
||
|
|
OptionalGetValue = tf_export("raw_ops.OptionalGetValue")(_ops.to_raw_op(optional_get_value))
|
||
|
|
|
||
|
|
|
||
|
|
def optional_get_value_eager_fallback(optional, output_types, output_shapes, name, ctx):
|
||
|
|
if not isinstance(output_types, (list, tuple)):
|
||
|
|
raise TypeError(
|
||
|
|
"Expected list for 'output_types' argument to "
|
||
|
|
"'optional_get_value' Op, not %r." % output_types)
|
||
|
|
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
|
||
|
|
if not isinstance(output_shapes, (list, tuple)):
|
||
|
|
raise TypeError(
|
||
|
|
"Expected list for 'output_shapes' argument to "
|
||
|
|
"'optional_get_value' Op, not %r." % output_shapes)
|
||
|
|
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
|
||
|
|
optional = _ops.convert_to_tensor(optional, _dtypes.variant)
|
||
|
|
_inputs_flat = [optional]
|
||
|
|
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
|
||
|
|
_result = _execute.execute(b"OptionalGetValue", len(output_types),
|
||
|
|
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
|
||
|
|
name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"OptionalGetValue", _inputs_flat, _attrs, _result)
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def optional_has_value(optional, name=None):
|
||
|
|
r"""Returns true if and only if the given Optional variant has a value.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
optional: A `Tensor` of type `variant`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `bool`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "OptionalHasValue", name, optional)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return optional_has_value_eager_fallback(
|
||
|
|
optional, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"OptionalHasValue", optional=optional, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ()
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"OptionalHasValue", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
OptionalHasValue = tf_export("raw_ops.OptionalHasValue")(_ops.to_raw_op(optional_has_value))
|
||
|
|
|
||
|
|
|
||
|
|
def optional_has_value_eager_fallback(optional, name, ctx):
|
||
|
|
optional = _ops.convert_to_tensor(optional, _dtypes.variant)
|
||
|
|
_inputs_flat = [optional]
|
||
|
|
_attrs = None
|
||
|
|
_result = _execute.execute(b"OptionalHasValue", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"OptionalHasValue", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def optional_none(name=None):
|
||
|
|
r"""Creates an Optional variant with no value.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `variant`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "OptionalNone", name)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return optional_none_eager_fallback(
|
||
|
|
name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"OptionalNone", name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ()
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"OptionalNone", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
OptionalNone = tf_export("raw_ops.OptionalNone")(_ops.to_raw_op(optional_none))
|
||
|
|
|
||
|
|
|
||
|
|
def optional_none_eager_fallback(name, ctx):
|
||
|
|
_inputs_flat = []
|
||
|
|
_attrs = None
|
||
|
|
_result = _execute.execute(b"OptionalNone", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"OptionalNone", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|