251 lines
9.2 KiB
Python
251 lines
9.2 KiB
Python
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"""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.security.fuzzing.py import annotation_types as _atypes
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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, List, Any
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from typing_extensions import Annotated
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def eager_py_func(input, token: str, Tout, is_async:bool=False, name=None):
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r"""Eagerly executes a python function to compute func(input)->output. The
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semantics of the input, output, and attributes are the same as those for
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PyFunc.
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Args:
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input: A list of `Tensor` objects.
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token: A `string`.
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Tout: A list of `tf.DTypes`.
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is_async: An optional `bool`. Defaults to `False`.
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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 `Tout`.
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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, "EagerPyFunc", name, input, "token", token, "is_async",
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is_async, "Tout", Tout)
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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 eager_py_func_eager_fallback(
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input, token=token, is_async=is_async, Tout=Tout, name=name,
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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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token = _execute.make_str(token, "token")
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if not isinstance(Tout, (list, tuple)):
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raise TypeError(
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"Expected list for 'Tout' argument to "
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"'eager_py_func' Op, not %r." % Tout)
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Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
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if is_async is None:
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is_async = False
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is_async = _execute.make_bool(is_async, "is_async")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"EagerPyFunc", input=input, token=token, Tout=Tout, is_async=is_async,
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name=name)
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_result = _outputs[:]
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if not _result:
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return _op
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if _execute.must_record_gradient():
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_attrs = ("token", _op.get_attr("token"), "is_async",
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_op._get_attr_bool("is_async"), "Tin", _op.get_attr("Tin"),
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"Tout", _op.get_attr("Tout"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"EagerPyFunc", _inputs_flat, _attrs, _result)
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return _result
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EagerPyFunc = tf_export("raw_ops.EagerPyFunc")(_ops.to_raw_op(eager_py_func))
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def eager_py_func_eager_fallback(input, token: str, Tout, is_async: bool, name, ctx):
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token = _execute.make_str(token, "token")
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if not isinstance(Tout, (list, tuple)):
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raise TypeError(
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"Expected list for 'Tout' argument to "
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"'eager_py_func' Op, not %r." % Tout)
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Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
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if is_async is None:
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is_async = False
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is_async = _execute.make_bool(is_async, "is_async")
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_attr_Tin, input = _execute.convert_to_mixed_eager_tensors(input, ctx)
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_inputs_flat = list(input)
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_attrs = ("token", token, "is_async", is_async, "Tin", _attr_Tin, "Tout",
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Tout)
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_result = _execute.execute(b"EagerPyFunc", len(Tout), 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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"EagerPyFunc", _inputs_flat, _attrs, _result)
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return _result
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def py_func(input, token: str, Tout, name=None):
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r"""Invokes a python function to compute func(input)->output.
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This operation is considered stateful. For a stateless version, see
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PyFuncStateless.
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Args:
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input: A list of `Tensor` objects.
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List of Tensors that will provide input to the Op.
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token: A `string`.
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A token representing a registered python function in this address space.
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Tout: A list of `tf.DTypes`. Data types of the outputs from the op.
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The length of the list specifies the number of outputs.
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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 `Tout`.
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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, "PyFunc", name, input, "token", token, "Tout", Tout)
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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 py_func_eager_fallback(
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input, token=token, Tout=Tout, 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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token = _execute.make_str(token, "token")
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if not isinstance(Tout, (list, tuple)):
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raise TypeError(
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"Expected list for 'Tout' argument to "
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"'py_func' Op, not %r." % Tout)
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Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"PyFunc", input=input, token=token, Tout=Tout, name=name)
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_result = _outputs[:]
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if not _result:
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return _op
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if _execute.must_record_gradient():
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_attrs = ("token", _op.get_attr("token"), "Tin", _op.get_attr("Tin"),
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"Tout", _op.get_attr("Tout"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"PyFunc", _inputs_flat, _attrs, _result)
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return _result
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PyFunc = tf_export("raw_ops.PyFunc")(_ops.to_raw_op(py_func))
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def py_func_eager_fallback(input, token: str, Tout, name, ctx):
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token = _execute.make_str(token, "token")
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if not isinstance(Tout, (list, tuple)):
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raise TypeError(
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"Expected list for 'Tout' argument to "
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"'py_func' Op, not %r." % Tout)
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Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
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_attr_Tin, input = _execute.convert_to_mixed_eager_tensors(input, ctx)
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_inputs_flat = list(input)
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_attrs = ("token", token, "Tin", _attr_Tin, "Tout", Tout)
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_result = _execute.execute(b"PyFunc", len(Tout), 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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"PyFunc", _inputs_flat, _attrs, _result)
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return _result
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def py_func_stateless(input, token: str, Tout, name=None):
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r"""A stateless version of PyFunc.
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Args:
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input: A list of `Tensor` objects.
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token: A `string`.
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Tout: A list of `tf.DTypes`.
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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 `Tout`.
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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, "PyFuncStateless", name, input, "token", token, "Tout", Tout)
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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 py_func_stateless_eager_fallback(
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input, token=token, Tout=Tout, 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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token = _execute.make_str(token, "token")
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if not isinstance(Tout, (list, tuple)):
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raise TypeError(
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"Expected list for 'Tout' argument to "
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"'py_func_stateless' Op, not %r." % Tout)
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Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"PyFuncStateless", input=input, token=token, Tout=Tout, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("token", _op.get_attr("token"), "Tin", _op.get_attr("Tin"),
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"Tout", _op.get_attr("Tout"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"PyFuncStateless", _inputs_flat, _attrs, _result)
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return _result
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PyFuncStateless = tf_export("raw_ops.PyFuncStateless")(_ops.to_raw_op(py_func_stateless))
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def py_func_stateless_eager_fallback(input, token: str, Tout, name, ctx):
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token = _execute.make_str(token, "token")
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if not isinstance(Tout, (list, tuple)):
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raise TypeError(
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"Expected list for 'Tout' argument to "
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"'py_func_stateless' Op, not %r." % Tout)
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Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
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_attr_Tin, input = _execute.convert_to_mixed_eager_tensors(input, ctx)
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_inputs_flat = list(input)
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_attrs = ("token", token, "Tin", _attr_Tin, "Tout", Tout)
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_result = _execute.execute(b"PyFuncStateless", len(Tout),
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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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"PyFuncStateless", _inputs_flat, _attrs, _result)
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return _result
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