text: # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # =============================================================================
  """Python front-end supports for functions.
  
  NOTE: At this time, functions are experimental and subject to change!. Proceed
  with caution.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import hashlib
  
  from tensorflow.core.framework import attr_value_pb2
  from tensorflow.core.framework import function_pb2
  from tensorflow.python import pywrap_tensorflow as c_api
  from tensorflow.python.eager import context
  from tensorflow.python.framework import c_api_util
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import graph_to_function_def
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import variable_scope as vs
  from tensorflow.python.util import compat
  from tensorflow.python.util import function_utils
  from tensorflow.python.util import tf_contextlib
  from tensorflow.python.util import tf_inspect
  
  
  class Defun(object):
    """Decorator used to define TensorFlow functions.
  
    Use this decorator to make a Python function usable directly as a TensorFlow
    function.
  
    The decorated function must add ops to the default graph and return zero or
    more `Tensor` objects.  Call the decorator with named arguments, one for each
    argument of the function to decorate, with the expected type of the argument
    as value.
  
    For example if the function to decorate accepts two `tf.float32` arguments
    named `x` and `y`, call the decorator with:
  
        @Defun(tf.float32, tf.float32)
        def foo(x, y):
          ...
  
    When you call the decorated function, it adds the `call` ops to the
    default graph. In addition, it adds the definition of the function into the
    default graph. Because the addition of the function into the graph
    is deferred, the decorator can be used anywhere in the program.
  
    Any variables created inside of the function are hoisted into the outer graph.
    Note that the variables are created in the variable scope that was active
    during the first call to the function. Subsequent function calls will refer to
    the same set of variables.
  
    Definitions of functions in a graph are frozen as soon as the graph is used to
    create a session. However, new functions and new calls to existing functions
    may be added to the graph, with the new functions themselves becoming
    immediately frozen.
  
    Example, but also see the [How To on functions](link_needed).
  
    ```python
    # Defining the function.
    @tf.Defun(tf.float32, tf.float32)
    def MyFunc(x, y):
      return x + y, x - y
  
    # Building the graph.
    a = tf.constant([1.0])
    b = tf.constant([2.0])
    c, d = MyFunc(a, b, name='mycall')
    ```
    """
  
    def __init__(self, *input_types, **kwargs):
      """Create a `Defun` decorator.
  
      Args:
        *input_types: A list of `tf.DType`
        **kwargs: Optional keyword arguments, including
           func_name - (optional).  A python string, the name to use to
             declare this `Function` in the graph.
  
           grad_func - (optional).  A function implementing the gradient
             of the function-to-register.  This is must be a
             `_DefinedFunction` object. The gradient
             function must satisfy the criterion defined in
             function.proto:GradientDef.
  
           python_grad_func - (optional).  A function implementing the
             gradient of the function python-side. This function must
             take the current op and the gradients w.r.t. its outputs,
             and return the gradients w.r.t. the inputs. That is it must
             implement the interface expected by `tf.RegisterGradient`).
             This will be called by tf.gradients to add the gradient ops
             to the graph. At most one of grad_func and python_grad_func
             can be specified.
  
           out_names = (optional). A list of strings, one per output
             tensor.
  
           shape_func - (optional). A function taking the op and returning a list
             of static shapes to set for the function's outputs.
      """
      self._input_types = input_types
      self._func_name = kwargs.pop("func_name", None)
      self._grad_func = kwargs.pop("grad_func", None)
      self._python_grad_func = kwargs.pop("python_grad_func", None)
      self._out_names = kwargs.pop("out_names", None)
      self._extra_kwargs = kwargs
  
    def __call__(self, func):
      # Various sanity checks on the callable func.
      if not callable(func):
        raise ValueError("function %s must be callable" % func)
  
      # Func should not use kwargs and defaults.
      argspec = tf_inspect.getargspec(func)
      if argspec.keywords or argspec.defaults:
        raise ValueError(
            "function with argument defaults or keywords arguments are not"
            " supported. {} has defaults {} and keywords {}.".format(
                func, argspec.defaults, argspec.keywords))
  
      # Computes how many arguments 'func' has.
      min_args = len(argspec.args)
      max_args = min_args
      if argspec.varargs:
        max_args = 1000000
      argnames = argspec.args
      if tf_inspect.ismethod(func):
        # 1st argument is the "class" type.
        min_args -= 1
        argnames = argnames[1:]
  
      if self._input_types:
        # If Defun is given a list of types for the inputs, the number
        # of input types should be compatible with 'func'.
        num = len(self._input_types)
        if num < min_args or num > max_args:
          raise ValueError(
              "The function has fewer arguments than the number of specified "
              "input types.")
        return _DefinedFunction(
            func,
            argnames,
            self._input_types,
            self._func_name,
            self._grad_func,
            self._python_grad_func,
            out_names=self._out_names,
            **self._extra_kwargs)
  
      # 'func' expects no arguments and input types is an empty list.
      if min_args == 0 and max_args == 0:
        return _DefinedFunction(
            func, [], [],
            self._func_name,
            self._grad_func,
            self._python_grad_func,
            out_names=self._out_names,
            **self._extra_kwargs)
  
      # Input types are unknown. It's an overloaded function and hence
      # its definition needs to be deferred until it's called.
      return _OverloadedFunction(
          func,
          argnames,
          self._func_name,
          self._grad_func,
          self._python_grad_func,
          out_names=self._out_names,
          **self._extra_kwargs)
  
  
  class _DefinedFunction(object):
    """_DefinedFunction encapsulates a function definition and its properties.
  
    Attributes:
      name: The function name.
      definition: The definition of this function. A FunctionDef proto.
      grad_func_name: If not None, the name of this function's gradient function.
      python_grad_func: A python callable implementing the gradient of
        the function python-side.
    """
  
    def __init__(self,
                 func,
                 argnames,
                 input_types,
                 func_name=None,
                 grad_func=None,
                 python_grad_func=None,
                 out_names=None,
                 shape_func=None,
                 capture_by_value=False,
                 whitelisted_stateful_ops=None,
                 capture_resource_var_by_value=True,
                 **kwargs):
      """Creates _DefinedFunction.
  
      Args:
        func:  A python callable which constructs a tf function body.
        argnames: A list of strings for function argument names.
        input_types: The function's argument types. Can be a tuple, list of
          tf data types.
        func_name: The function name. Defaults to None, in which derives from
          'func'.
        grad_func: This function's gradient function, if not None. Defaults
          to None.
        python_grad_func: A python callable implementing the gradient of
          the function python-side.
        out_names: An optional list of strings for the function return value
          names.
        shape_func: An optional function mapping an op to a list of static
          output shapes.
        capture_by_value: Boolean (defaults to False). If True, captured values
          will be copied into the function body.
        whitelisted_stateful_ops: A set of ops that if stateful we ignore and
          copy into the function body, when `capture_by_value` is True.
        capture_resource_var_by_value: Boolean (defaults to True). If False,
          captured resource variable returns the handle instead of value.
        **kwargs: The keyword arguments. **kwargs is passed to every call
          site of this function.
  
      Raises:
        ValueError: The function definition is invalid.
  
      """
      self._func = func
      self._input_types = input_types
      self._func_name = func_name
      self._grad_func = grad_func
      self._python_grad_func = python_grad_func
      self._out_names = out_names
      self._shape_func = shape_func
      self._capture_by_value = capture_by_value
      self._whitelisted_stateful_ops = whitelisted_stateful_ops
      if self._whitelisted_stateful_ops is None:
        self._whitelisted_stateful_ops = set()
      self._capture_resource_var_by_value = capture_resource_var_by_value
      self._extra_kwargs = kwargs
      # Constructed only when C API is disabled, lazily
      self._definition = None
      # Constructed only when C API is enabled, lazily
      self._c_func = None
      self._sub_functions = {}  # Constructed with _definition or _c_func
      # pylint: disable=protected-access
      device_funcs = ops.get_default_graph()._device_functions_outer_to_inner
      # pylint: enable=protected-access
  
      # Get the innermost device if possbile.
      self._caller_device = device_funcs[-1] if device_funcs else None
  
      # Cached OpDef for this function. When C API is enabled, this is
      # the only part of FunctionDef that we cache in Python. When C API
      # is disabled the whole _definition is available and this is simply
      # another reference to _definition.signature
      self._op_def = None
  
      assert isinstance(input_types, (list, tuple))
      self._arg_types = input_types
      self._arg_names = [argnames[i] if i < len(argnames) else ("arg%d" % i)
                         for i in range(len(input_types))]
  
    @property
    def name(self):
      """Function name."""
      self._create_definition_if_needed()
      return self._func_name
  
    @property
    def definition(self):
      """Function definition proto."""
      self._create_definition_if_needed()
      if self._c_func:
        with c_api_util.tf_buffer() as buf:
          c_api.TF_FunctionToFunctionDef(self._c_func.func, buf)
          fdef = function_pb2.FunctionDef()
          proto_data = c_api.TF_GetBuffer(buf)
          fdef.ParseFromString(compat.as_bytes(proto_data))
        return fdef
      return self._definition
  
    @property
    def _signature(self):
      self._create_definition_if_needed()
      return self._op_def
  
    def set_grad_func(self, grad_func):
      """Specifies the gradient function of this function."""
      assert not self._grad_func
      assert isinstance(grad_func, _DefinedFunction)
      self._grad_func = grad_func
  
    @property
    def grad_func_name(self):
      """Returns the name of the gradient function."""
      return self._grad_func.name if self._grad_func else None
  
    @property
    def python_grad_func(self):
      """Python gradient function callable."""
      return self._python_grad_func
  
    @property
    def declared_input_types(self):
      """Returns the list of data types of explicit declared inputs."""
      return self._input_types
  
    @property
    def captured_inputs(self):
      """Returns the list of implicitly captured inputs."""
      self._create_definition_if_needed()
      return self._extra_inputs
  
    @property
    def stateful_ops(self):
      """Returns the list of stateful ops in function definition.
  
      Returns:
        A list of (op.name, op.type) pairs.
      """
      self._create_definition_if_needed()
      return self._stateful_ops
  
    def _create_definition_if_needed(self):
      """Creates the function definition if it's not created yet."""
      with context.graph_mode():
        self._create_definition_if_needed_impl()
  
    def _create_definition_if_needed_impl(self):
      """This is not what you want, see _create_definition_if_needed."""
      if self._definition is not None or self._c_func is not None:
        return
  
      # Copy variable collections (by reference) from the parent graph such that
      # name based variable sharing (e.g. via tf.make_template) works between the
      # func graph and parent graph.
      variable_keys = []
      variable_keys.extend(ops.GraphKeys._VARIABLE_COLLECTIONS)  # pylint: disable=protected-access
      variable_keys.append(vs._VARSTORE_KEY)  # pylint: disable=protected-access
  
      collections_ref = {}
      parent_collections_ref = ops.get_default_graph()._collections  # pylint: disable=protected-access
      for key in variable_keys:
        if key not in parent_collections_ref:
          parent_collections_ref[key] = collections_ref[key] = []
        else:
          collections_ref[key] = parent_collections_ref[key]
  
      temp_graph = func_graph_from_py_func(
          self._func,
          self._arg_names,
          self._arg_types,
          self._func_name,
          self._capture_by_value,
          self._caller_device,
          collections_ref=collections_ref,
          whitelisted_stateful_ops=self._whitelisted_stateful_ops,
          capture_resource_var_by_value=self._capture_resource_var_by_value)
  
      self._extra_inputs = temp_graph.extra_inputs
      # pylint: disable=protected-access
      self._sub_functions = temp_graph._functions
      # pylint: enable=protected-access
  
      # Extra kwargs are treated as attrs on the function def.
      if self._func_name:
        base_func_name = self._func_name
      else:
        base_func_name = function_utils.get_func_name(self._func)
        if self._grad_func:
          base_func_name += ("_%s" % self._grad_func.name)
      kwargs_attr = _parse_kwargs_as_attrs(base_func_name, **self._extra_kwargs)
  
      if not temp_graph._c_graph:  # pylint: disable=protected-access
        # Build the FunctionDef
        self._definition = graph_to_function_def.graph_to_function_def(
            temp_graph,
            temp_graph.get_operations(),
            temp_graph.inputs,
            temp_graph.outputs,
            out_names=self._out_names)
  
        for k in kwargs_attr:
          self._definition.attr[k].CopyFrom(kwargs_attr[k])
  
        # Hash the definition and its dependencies.
        self._hash_str = self._create_hash_str(
            self._definition.signature.input_arg,
            self._definition.signature.output_arg, self._definition.node_def)
  
        # Finally, we decide the function name to use.  If not specified,
        # make up something which is almost certainly unique (but deterministic).
        if not self._func_name:
          self._func_name = "_".join([base_func_name, self._hash_str])
        self._definition.signature.name = self._func_name
        if self._func.__doc__:
          self._definition.signature.description = self._func.__doc__
  
        self._op_def = self._definition.signature
      else:  # C API is enabled
        output_names = ([compat.as_bytes(x) for x in self._out_names]
                        if self._out_names else [])
        description = self._func.__doc__ or None
        # pylint: disable=protected-access
        c_func = c_api.TF_GraphToFunction_wrapper(
            temp_graph._c_graph,
            base_func_name,
            self._func_name is None,  # append_hash_to_fn_name
            None,  # opers
            [t._as_tf_output() for t in temp_graph.inputs],
            [t._as_tf_output() for t in temp_graph.outputs],
            output_names,
            [], # control_outputs
            [], # control_output_names
            None,  # opts
            description)
        self._c_func = c_api_util.ScopedTFFunction(c_func)
        # pylint: enable=protected-access
        self._set_c_attrs(kwargs_attr)
  
        # Set cached fields: _op_def and _func_name (if not already set)
        self._op_def = self.definition.signature
        if self._func_name:
          assert self._func_name == self._op_def.name
        else:
          self._func_name = compat.as_str(self._op_def.name)
  
      self._stateful_ops = [(op.name, op.type)
                            for op in temp_graph.get_operations()
                            if op.op_def.is_stateful]
  
    def _set_c_attrs(self, attrs):
      """Sets `attrs` as attributes of self._c_func.
  
      Requires that self._c_func is not None.
  
      Args:
        attrs: a dictionary from attribute name to attribute proto value
      """
      for name, attr_value in attrs.items():
        serialized = attr_value.SerializeToString()
        # TODO(skyewm): this creates and deletes a new TF_Status for every attr.
        # It might be worth creating a convenient way to re-use the same status.
        c_api.TF_FunctionSetAttrValueProto(self._c_func.func, compat.as_str(name),
                                           serialized)
  
    def _create_hash_str(self, input_arg, output_arg, node_def):
      """Creates an 8-character string unique to this input.
  
      Args:
        input_arg: the input_arg field of an OpDef
                   (e.g. self._definition.signature.input_arg)
        output_arg: the output_arg field of an OpDef
                   (e.g. self._definition.signature.output_arg)
        node_def: the node_def field of a FunctionDef
                  (e.g. self._definition.node_def)
  
      Returns:
        The unique string for this input
      """
      hasher = hashlib.sha1()
  
      def update_num(n):
        hasher.update(compat.as_bytes("%x" % n))
  
      def update_str(s):
        update_num(len(s))
        hasher.update(compat.as_bytes(s))
  
      def update_strs(slist):
        update_num(len(slist))
        for s in slist:
          update_str(s)
  
      for adef in input_arg:
        update_str(adef.SerializeToString())
  
      for adef in output_arg:
        update_str(adef.SerializeToString())
  
      for n in sorted(node_def, key=lambda n: n.name):
        update_str(n.name)
        update_str(n.op)
        update_strs(n.input)
        update_num(len(n.attr))
        # NOTE: protobuf map serialization does not guarantee ordering.
        for k in sorted(n.attr):
          update_str(k)
          update_str(n.attr[k].SerializeToString())
  
      return hasher.hexdigest()[:8]
  
    def add_to_graph(self, g):
      """Adds this function into the graph g."""
      self._create_definition_if_needed()
  
      # Adds this function into 'g'.
      # pylint: disable=protected-access
      if context.executing_eagerly():
        context.context().add_function_def(self.definition)
      else:
        g._add_function(self)
      # pylint: enable=protected-access
  
      # Ensures related sub-routines are defined in 'g', too.
      for f in self._sub_functions.values():
        f.add_to_graph(g)
  
      # Adds its gradient function, too.
      if self._grad_func:
        self._grad_func.add_to_graph(g)
  
    def __call__(self, *args, **kwargs):
      self.add_to_graph(ops.get_default_graph())
      args = [ops.convert_to_tensor(_) for _ in args] + self._extra_inputs
      ret, op = _call(self._signature, *args, **kwargs)
  
      # Set a hidden attr in 'op' so that gradients_impl can refer back
      # to this _DefinedFunction instance to access python_grad_func.
      assert isinstance(op, ops.Operation)
      setattr(op, "__defun", self)
  
      if self._shape_func is not None:
        shapes = self._shape_func(op)
        if len(shapes) != len(op.outputs):
          raise ValueError("shape_func produced %d shapes for %d outputs" %
                           (len(shapes), len(op.outputs)))
        for (t, shape) in zip(op.outputs, shapes):
          t.set_shape(shape)
      return ret
  
  
  class _OverloadedFunction(object):
    """_OverloadedFunction encapsulates an overloaded function.
  
    _OverloadedFunction maintains a mapping from input types to
    instantiated _DefinedFunction in self._overload.
  
    """
  
    def __init__(self,
                 func,
                 argnames,
                 func_name=None,
                 grad_func=None,
                 python_grad_func=None,
                 out_names=None,
                 **kwargs):
      """Creates _DefinedFunction.
  
      Args:
        func:  A python callable which constructs a tf function body.
        argnames: A list of strings for function argument names.
        func_name: The function name. Defaults to None, in which derives from
          'func'.
        grad_func: This function's gradient function, if not None. Defaults
          to None.
        python_grad_func: A python callable implementing the gradient of
          the function python-side.
        out_names: A list of strings for the function return value names.
        **kwargs: The keyword arguments. **kwargs is passed to every call
          site of this function.
  
      Raises:
        ValueError: The function definition is invalid.
  
      """
      self._func = func
      self._argnames = argnames
      self._func_name = func_name
      assert grad_func is None or isinstance(grad_func, _OverloadedFunction)
      self._grad_func = grad_func
      self._python_grad_func = python_grad_func
      self._out_names = out_names
      self._extra_kwargs = kwargs
      self._overload = {}
  
    def instantiate(self, input_types):
      """Instantiate this function given input argument types.
  
      Args:
        input_types: A list of data types for the inputs.
  
      Returns:
        _DefinedFunction for the given input types.
  
      """
      # Stringify the type list.
      key = _type_list_to_str(input_types)
      defined = self._overload.get(key)
      if not defined:
        # If not defined yet, define the function given the input types.
        name = self._func_name
        if name is not None:
          name = "_".join([name, key])
        defined = _DefinedFunction(
            self._func,
            self._argnames,
            input_types,
            name,
            None,
            self._python_grad_func,
            out_names=self._out_names,
            **self._extra_kwargs)
        _ = defined.name  # Fully instantiate the function definition.
        if self._grad_func:
          # If _grad_func is given, it is another
          # _OverloadedFunction. We need to instantiate it with the
          # right input types.
          output_types = [
              dtypes.DType(_.type) for _ in defined._signature.output_arg  # pylint: disable=protected-access
          ]
          # pylint: disable=protected-access
          defined._grad_func = self._grad_func.instantiate(input_types +
                                                           output_types)
          # pylint: enable=protected-access
        self._overload[key] = defined
      return defined
  
    def __call__(self, *args, **kwargs):
      input_types = []
      args = list(args)
      for (i, x) in enumerate(args):
        x = ops.convert_to_tensor(x)
        if not isinstance(x, ops.Tensor):
          raise ValueError("Expect a Tensor but get ", x)
        input_types.append(x.dtype)
        args[i] = x
      return self.instantiate(input_types)(*args, **kwargs)
  
  
  class _FuncGraph(ops.Graph):
    """A helper for constructing a function.
  
    _FuncGraph overrides ops.Graph's create_op() so that we can keep
    track of all inputs into every op created inside the function.  If
    any input is from other graphs, we keep track of it in self.capture
    and substitute the input with a place holder.
  
    Each captured input's corresponding place holder is converted into a
    function argument and the caller passes in the captured tensor.
    """
  
    def __init__(self, name, capture_by_value, whitelisted_stateful_ops,
                 capture_resource_var_by_value, *args, **kwargs):
      super(_FuncGraph, self).__init__(*args, **kwargs)
      self._capture_by_value = capture_by_value
      self._whitelisted_stateful_ops = whitelisted_stateful_ops
      self._capture_resource_var_by_value = capture_resource_var_by_value
      self._building_function = True
      self._outer_graph = ops.get_default_graph()
      self._vscope = vs.get_variable_scope()
      self._old_custom_getter = self._vscope.custom_getter
  
      # The name of the function.
      self.name = name
      # Placeholder tensors representing the inputs to this function. The tensors
      # are in this _FuncGraph.
      self.inputs = []
      # Tensors that will be returned this function. The tensors are in this
      # _FuncGraph.
      self.outputs = []
      # Maps external tensor -> internal tensor (e.g. input placeholder).
      self._captured = {}
      # The external tensors that have been captured as inputs and must be passed
      # to this function (empty if capturing by value, otherwise these are the
      # keys of _captured).
      self.extra_inputs = []
      # Input placeholders that been added for captured values (empty if capturing
      # by value).
      self.extra_args = []
      # Captured variables.
      # TODO(skyewm): is this needed?
      self.extra_vars = []
  
    # pylint: disable=g-doc-return-or-yield
  
    @tf_contextlib.contextmanager
    def container(self, container_name):
      """Returns a context manager that specifies the resource container to use.
  
      Overridden from `tf.Graph` to update both the init_scope container
      and the present inner container. This is necessary to make sure setting
      containers applies correctly both to created variables and to stateful
      ops.
  
      Args:
        container_name: container name string.
  
      Returns:
        A context manager for defining resource containers for stateful ops,
          yields the container name.
      """
      original_container = self._container
      # pylint: disable=protected-access
      with ops.init_scope():
        original_init_container = ops.get_default_graph()._container
      try:
        self._container = container_name
        with ops.init_scope():
          ops.get_default_graph()._container = container_name
        yield self._container
      finally:
        self._container = original_container
        with ops.init_scope():
          ops.get_default_graph()._container = original_init_container
      # pylint: enable=protected-access
  
    # pylint: enable=g-doc-return-or-yield
  
    def getvar(
        self,
        getter,
        name,
        shape=None,
        dtype=None,
        initializer=None,
        reuse=None,
        trainable=True,
        collections=None,  # pylint: disable=redefined-outer-name
        use_resource=None,
        **kwargs):
      """A custom variable getter."""
      # Here, we switch the default graph to the outer graph and ask the
      # variable scope in which the function is defined to give us the
      # variable. The variable is stashed in extra_vars and returned to
      # the caller.
      #
      # We capture these variables so that the variable definition is
      # hoisted upward to the outer most graph.
      with self._outer_graph.as_default():
        # pylint: disable=protected-access
        var = self._vscope.get_variable(
            vs._get_default_variable_store(),
            name,
            shape=shape,
            dtype=dtype,
            initializer=initializer,
            reuse=reuse,
            trainable=trainable,
            collections=collections,
            use_resource=use_resource)
        self.extra_vars.append(var)
        if (isinstance(var, resource_variable_ops.ResourceVariable) and
            self._capture_resource_var_by_value):
          # For resource-based variables read the variable outside the function
          # and pass in the value. This ensures that the function is pure and
          # differentiable. TODO(apassos) this may have performance problems if
          # the function will only do embedding lookups on the variable.
          return var.value()
        return var
  
    def create_op(self, op_type, inputs, dtypes=None, **kwargs):  # pylint: disable=redefined-outer-name
      for i, x in enumerate(inputs):
        if isinstance(x, ops.EagerTensor) or x.graph is not self:
          inputs[i] = self.capture(x)
      return super(_FuncGraph, self).create_op(op_type, inputs,
                                               dtypes=dtypes, **kwargs)
  
    def capture(self, tensor, name=None):
      """Adds the given tensor to this graph and returns the captured tensor."""
      if tensor in self._captured:
        # Captured already.
        return self._captured[tensor]
      elif self._capture_by_value:
        return self._add_tensor_and_parents(tensor)
      else:
        return self._capture_tensor_as_extra_input(tensor, name)
  
    def _capture_tensor_as_extra_input(self, tensor, name=None):
      # Substitute with a placeholder.
      self.extra_inputs.append(tensor)
      # Hoist the new input placeholder out of any control flow context
      # we're currently in.
      with ops.control_dependencies(None):
        ph = array_ops.placeholder(
            tensor.dtype, shape=tensor.get_shape(), name=name)
      # pylint: disable=protected-access
      if isinstance(tensor, ops.EagerTensor):
        handle_data = tensor._handle_data
        if handle_data:
          handle_data = handle_data.SerializeToString()
      else:
        handle_data = c_api.GetHandleShapeAndType(tensor.graph._c_graph,
                                                  tensor._as_tf_output())
  
      if handle_data:
        c_api.SetHandleShapeAndType(ph.graph._c_graph, ph._as_tf_output(),
                                    compat.as_bytes(handle_data))
      # pylint: enable=protected-access
      self.inputs.append(ph)
      self._captured[tensor] = ph
      self.extra_args.append(ph)
      if _is_guaranteed_const(tensor):
        with ops.control_dependencies(None):
          return array_ops.guarantee_const(ph)
      else:
        return ph
  
    def _add_tensor_and_parents(self, tensor):
      op = self._add_op_and_parents(tensor.op)
      return op.outputs[tensor.value_index]
  
    def _add_op_and_parents(self, op):
      # pylint: disable=protected-access
      op_def = graph_to_function_def._get_op_def(op)
      # pylint: enable=protected-access
      if op_def.is_stateful and op not in self._whitelisted_stateful_ops:
        raise ValueError("Cannot capture a stateful node (name:%s, type:%s) "
                         "by value." % (op.name, op.type))
      elif op.type in ("Placeholder", "PlaceholderV2"):
        raise ValueError("Cannot capture a placeholder (name:%s, type:%s) "
                         "by value." % (op.name, op.type))
  
      captured_inputs = [self._add_tensor_and_parents(x) for x in op.inputs]
  
      captured_op = self.create_op(
          op.type,
          captured_inputs, [o.dtype for o in op.outputs],
          name=op.name,
          attrs=op.node_def.attr,
          op_def=op_def)
  
      for t, captured_t in zip(op.outputs, captured_op.outputs):
        self._captured[t] = captured_t
  
      return captured_op
  
  
  def func_graph_from_py_func(func,
                              arg_names,
                              arg_types,
                              name=None,
                              capture_by_value=False,
                              device=None,
                              colocation_stack=None,
                              container=None,
                              collections_ref=None,
                              arg_shapes=None,
                              whitelisted_stateful_ops=None,
                              capture_resource_var_by_value=True):
    """Returns a _FuncGraph generated from `func`.
  
    Args:
      func: A Python callable which constructs a TF function body. The arguments
        must correspond to `arg_types`. Returns a value or list/tuple of values.
        No returned value can be None.
      arg_names: A sequence of strings for the function argument names.
      arg_types: A sequence of the function's argument types.
      name: The function name. If None, the name is derived from `func`.
      capture_by_value: boolean. If True, captured values will be copied into the
        function body.
      device: device name or function.
      colocation_stack: A colocation stack (list) the _FuncGraph should use.
      container: A container name the _FuncGraph should start with.
      collections_ref: A reference to a collections dict the _FuncGraph should
        use internally.
      arg_shapes: A sequence of the function's argument shapes.
      whitelisted_stateful_ops: A set of ops that if stateful we ignore and
        re-create.
      capture_resource_var_by_value: Boolean (defaults to True). If False,
        captured resource variable returns the handle instead of value.
  
    Returns:
      A _FuncGraph.
  
    Raises:
      ValueError: if func returns None.
    """
    if not name:
      name = function_utils.get_func_name(func)
    func_graph = _FuncGraph(name, capture_by_value, whitelisted_stateful_ops,
                            capture_resource_var_by_value)
  
    with func_graph.as_default(), ops.device(device):
      # pylint: disable=protected-access
      if collections_ref is not None:
        func_graph._collections = collections_ref
      if container is not None:
        func_graph._container = container
      if colocation_stack is not None:
        func_graph._colocation_stack = colocation_stack
      # pylint: enable=protected-access
  
      if arg_shapes is None:
        arg_shapes = [None] * len(arg_types)
  
      # Create placeholders for the function arguments.
      for (argname, argtype, argshape) in zip(arg_names, arg_types, arg_shapes):
        argholder = array_ops.placeholder(argtype, shape=argshape, name=argname)
        func_graph.inputs.append(argholder)
      # Call func and gather the output tensors.
      with vs.variable_scope("", custom_getter=func_graph.getvar):
        outputs = func(*func_graph.inputs)
  
      # There is no way of distinguishing between a function not returning
      # anything and a function returning None in Python.
      # We need to allow the former and ideally want to forbid the latter as
      # it is most likely user error.
      # TODO(iga): Consider adding a @NoOutput decorator on top of @Defun to
      # allow users to explicitly mark the function as not returning anything.
      # For now, we allow a single None return and interpret it as a function
      # with no output.
      if outputs is None:
        outputs = []
      else:
        # If func only returned one value, make it a tuple.
        if not isinstance(outputs, (list, tuple)):
          outputs = (outputs,)
        if any(_ is None for _ in outputs):
          raise ValueError("Function %s can not return None." % name)
      # Ensures each output is a Tensor in the function graph.
      outputs = [ops.convert_to_tensor(t) for t in outputs]
      outputs = [func_graph.capture(t) if t.graph is not func_graph else t
                 for t in outputs]
      func_graph.outputs = outputs
    return func_graph
  
  
  def _is_guaranteed_const(tensor):
    """Determines whether `tensor` is guaranteed to be a constant.
  
    A tensor is guaranteed to be a constant if either it was produced by
    a `GuaranteeConst` op or if all of its children are guaranteed to be
    constants.
  
    Args:
      tensor: The tensor for which to determine const-ness.
  
    Returns:
      True if `tensor` is guaranteed to be a constant, False otherwise.
    """
  
    if isinstance(tensor, ops.EagerTensor):
      return False
  
    class Work(object):
  
      def __init__(self, op, leaving):
        self.op = op
        self.leaving = leaving
  
    is_guaranteed_const = lambda op: op.node_def.op == "GuaranteeConst"
    constants = set([])
    def all_inputs_const(op):
      # If all inputs of an op are guaranteed constants, then we can infer that
      # the op produces a constant as well.
      return op.inputs and all(inp.op in constants for inp in op.inputs)
  
    visited = set([])
    stack = [Work(tensor.op, leaving=False)]
    while stack:
      work = stack.pop()
      if work.leaving:
        if all_inputs_const(work.op):
          constants.add(work.op)
        continue
      visited.add(work.op)
      if is_guaranteed_const(work.op):
        constants.add(work.op)
        continue
  
      # This op will be revisited after all its inputs are checked for const-ness.
      stack.append(Work(work.op, leaving=True))
      for inp in work.op.inputs:
        if inp.op not in visited:
          stack.append(Work(inp.op, leaving=False))
    return tensor.op in constants
  
  
  def _call(sig, *inputs, **kwargs):
    """Adds a node calling a function.
  
    This adds a `call` op to the default graph that calls the function
    of signature `sig`, passing the tensors in `inputs` as arguments.
    It returns the outputs of the call, which are one or more tensors.
  
    `sig` is OpDefArg.a `_DefinedFunction` object.
  
    You can pass an optional keyword parameter `name=string` to name the
    added operation.
  
    You can pass an optional keyword parameter `noinline=True|False` to
    instruct the runtime not to inline the function body into the call
    site.
  
    Args:
      sig: OpDefArg. The signature of the function.
      *inputs: arguments to the function.
      **kwargs: Optional keyword arguments.  Can only contain 'name' or
          'noinline'.
  
    Returns:
       A 2-element tuple. First element: a Tensor if the function returns a single
       value; a list of Tensors if the function returns multiple value; the
       Operation if the function returns no values. Second element: the Operation.
  
    Raises:
      ValueError: if the arguments are invalid.
    """
    if len(inputs) != len(sig.input_arg):
      raise ValueError("Expected number of arguments: %d, received: %d" % (len(
          sig.input_arg), len(inputs)))
    name = kwargs.pop("name", None)
    g = ops.get_default_graph()
    func_name = sig.name
    if name is None:
      name = func_name
    attrs = _parse_kwargs_as_attrs(func_name, **kwargs)
    output_types = [dtypes.DType(x.type) for x in sig.output_arg]
    op = g.create_op(
        func_name, list(inputs), output_types, name=name, attrs=attrs, op_def=sig)
    if op.outputs:
      if len(op.outputs) == 1:
        ret = op.outputs[0]
      else:
        ret = tuple(op.outputs)
    else:
      ret = op
    return ret, op
  
  
  def _from_definition(fdef, grad_func=None):
    """Creates a _DefinedFunction initialized from a FunctionDef proto.
  
    Args:
      fdef: a FunctionDef
      grad_func: a _DefinedFunction or None
  
    Returns:
      A _DefinedFunction representing fdef
    """
    # TODO(iga): This method does major surgery on _DefinedFunction.
    # Make it a named constructor using @classmethod of _DefinedFunction.
  
    # The Python callable is only needed to create a FunctionDef. Since we have
    # the FunctionDef here, we don't need to set _DefinedFunction._func (nor do we
    # have access to such a callable here).
    func = None
    argnames = [arg.name for arg in fdef.signature.input_arg]
    input_types = tuple(
        dtypes.as_dtype(arg.type) for arg in fdef.signature.input_arg)
    func_name = fdef.signature.name
    # Note: FunctionDefs do not include python gradient functions, so if the
    # original _DefinedFunction included one it will not be reflected here.
    python_grad_func = None
    out_names = [arg.name for arg in fdef.signature.output_arg]
    result = _DefinedFunction(func, argnames, input_types, func_name, grad_func,
                              python_grad_func, out_names)
    # pylint: disable=protected-access
    serialized = fdef.SerializeToString()
    c_func = c_api.TF_FunctionImportFunctionDef(serialized)
    result._c_func = c_api_util.ScopedTFFunction(c_func)
    result._extra_inputs = []
    result._op_def = fdef.signature
    # pylint: enable=protected-access
  
    return result
  
  
  def from_library(lib):
    """Creates _DefinedFunctions initialized from a FunctionDefLibrary proto.
  
    This method handles assigning the correct gradient functions to each
    function.
  
    Args:
      lib: a FunctionDefLibrary
  
    Returns:
      A list of _DefinedFunctions
  
    Raises:
      ValueError: `lib` is invalid
    """
    if not lib.function and not lib.gradient:
      return []
  
    # function name -> FunctionDef proto
    funcs = {fdef.signature.name: fdef for fdef in lib.function}
  
    # Validate that all references function names have function defs
    for g in lib.gradient:
      if g.function_name not in funcs:
        raise ValueError("FunctionDefLibrary missing '%s' FunctionDef\n%s" %
                         (g.function_name, str(lib)))
      if g.gradient_func not in funcs:
        raise ValueError("FunctionDefLibrary missing '%s' FunctionDef\n%s" %
                         (g.gradient_func, str(lib)))
  
    # function name -> gradient function name
    func_to_grad = collections.defaultdict(lambda: None)
    # gradient function name -> names of functions having that grad function
    grad_to_funcs = collections.defaultdict(list)
  
    for gdef in lib.gradient:
      func_to_grad[gdef.function_name] = gdef.gradient_func
      grad_to_funcs[gdef.gradient_func].append(gdef.function_name)
  
    # Start with functions without gradients
    ready = [
        fdef for fdef in lib.function if func_to_grad[fdef.signature.name] is None
    ]
    if not ready:
      raise ValueError(
          "FunctionDefLibrary contains cyclic gradient functions!\n" + str(lib))
    # function name -> _DefinedFunction
    initialized = {}
  
    while ready:
      fdef = ready.pop()
      name = fdef.signature.name
  
      grad = initialized.get(func_to_grad[name])
      if func_to_grad[name]:
        assert grad
      defined_func = _from_definition(fdef, grad_func=grad)
      initialized[name] = defined_func
  
      ready.extend(funcs[f] for f in grad_to_funcs[name])
  
    return initialized.values()
  
  
  def _get_experimental_kwarg_as_attr(attr_name, value):
    """Creates an AttrValue for a python object."""
    if isinstance(value, bool):
      return attr_value_pb2.AttrValue(b=value)
    elif isinstance(value, int):
      return attr_value_pb2.AttrValue(i=value)
    elif isinstance(value, float):
      return attr_value_pb2.AttrValue(f=value)
    elif isinstance(value, str):
      return attr_value_pb2.AttrValue(s=compat.as_bytes(value))
    else:
      raise ValueError("Unsupported attribute type for %s with type %s" %
                       (attr_name, type(value)))
  
  
  def _parse_kwargs_as_attrs(func_name, **kwargs):
    """Parses **kwargs into a node's attributes."""
    attrs = {}
  
    noinline = kwargs.pop("noinline", None)
    if noinline is not None:
      attrs["_noinline"] = attr_value_pb2.AttrValue(b=bool(noinline))
  
    # For compatibility with previous behavior, Defun does not perform shape
    # inference through its function call operations.
    attrs["_disable_call_shape_inference"] = attr_value_pb2.AttrValue(b=True)
  
    compiled = kwargs.pop("compiled", None)
    separate_compiled_gradients = kwargs.pop("separate_compiled_gradients", None)
    if compiled is not None:
      attrs["_XlaCompile"] = attr_value_pb2.AttrValue(b=bool(compiled))
      attrs["_XlaSeparateCompiledGradients"] = attr_value_pb2.AttrValue(
          b=bool(separate_compiled_gradients))
      # Forward _XlaScope from enclosing context (if set), otherwise create new.
      # pylint: disable=protected-access
      if "_XlaScope" in ops.get_default_graph()._attr_scope_map:
        attrs["_XlaScope"] = ops.get_default_graph()._attr_scope_map["_XlaScope"]
      else:
        attrs["_XlaScope"] = attr_value_pb2.AttrValue(
            s=("function_%s" % func_name).encode())
      # pylint: enable=protected-access
  
    kwargs_keys = list(kwargs.keys())
    for key in kwargs_keys:
      if key.startswith("experimental_"):
        attrs[key] = _get_experimental_kwarg_as_attr(key, kwargs[key])
        del kwargs[key]
  
    if kwargs:
      raise ValueError("Unknown keyword arguments: %s" % kwargs.keys())
    return attrs
  
  
  def get_extra_vars():
    """Returns the captured variables by the function.
  
    Returns:
      If the default graph is being used to define a function, the
      returned list of variables are those created inside the function
      body so far. Otherwise, returns an empty list.
    """
    g = ops.get_default_graph()
    if isinstance(g, _FuncGraph):
      return g.extra_vars
    else:
      return []
  
  
  def get_extra_inputs():
    """Returns the captured input tensors by the function.
  
    Returns:
      If the default graph is being used to define a function, the
      returned list of tensors are those accessed inside the function body
      but defined outside the function body so far. Otherwise, returns an
      empty list.
    """
    g = ops.get_default_graph()
    if isinstance(g, _FuncGraph):
      return g.extra_inputs
    else:
      return []
  
  
  def get_extra_args():
    """Returns the corresponding function arguments for the captured inputs.
  
    Returns:
      If the default graph is being used to define a function, the
      returned list of place holders are those used inside the function
      body corresponding those returned by get_extra_inputs(). Otherwise,
      returns an empty list.
    """
    g = ops.get_default_graph()
    if isinstance(g, _FuncGraph):
      return g.extra_args
    else:
      return []
  
  
  def _type_list_to_str(types):
    if any(_ not in _DTYPE_TO_STR for _ in types):
      raise ValueError("Unsupported dtypes: %s" % types)
    return "".join([_DTYPE_TO_STR[_] for _ in types])
  
  
  # NOTE: The list needs to be extended when more data types are added.
  _DTYPE_TO_STR = {
      dtypes.float16: "f16",
      dtypes.float32: "f32",
      dtypes.float64: "f64",
      dtypes.int32: "i32",
      dtypes.uint8: "i8",
      dtypes.uint16: "u16",
      dtypes.uint32: "u32",
      dtypes.uint64: "u64",
      dtypes.int16: "i16",
      dtypes.int8: "i8",
      dtypes.string: "s",
      dtypes.complex64: "c64",
      dtypes.complex128: "c128",
      dtypes.int64: "i64",
      dtypes.bool: "b",
      dtypes.qint8: "qi8",
      dtypes.quint8: "qu8",
      dtypes.qint16: "qi16",
      dtypes.quint16: "qu16",
      dtypes.qint32: "qi32",
      dtypes.bfloat16: "b16"
  }
  
  
  def function_def_from_tf_function(c_func):
    """Converts a SWIG-wrapped TF_Function* to a FunctionDef proto."""
    with c_api_util.tf_buffer() as buf:
      c_api.TF_FunctionToFunctionDef(c_func, buf)
      data = c_api.TF_GetBuffer(buf)
    fdef = function_pb2.FunctionDef()
    fdef.ParseFromString(compat.as_bytes(data))
    return fdef
  