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