parameters: (self,
                 python_function,
                 name,
                 input_signature=None,
                 autograph=True,
                 experimental_autograph_options=None,
                 experimental_relax_shapes=False,
                 experimental_compile=None)
docstring: Initializes a `Function`.
  
      Args:
        python_function: the function to be wrapped.
        name: the name given to it.
        input_signature: a possibly nested sequence of `TensorSpec` objects
          specifying the input signature of this function. If `None`, a separate
          function is instantiated for each inferred input signature.
        autograph: whether `python_function` should be converted to graph mode.
          See https://www.tensorflow.org/guide/autograph for more information.
        experimental_autograph_options: optional tuple of
          tensorflow.autograph.Feature values. Allows enabling additional
          conversion options when autograph is set to True.
        experimental_relax_shapes: When true, argument shapes may be relaxed to
          avoid unecessary retracing.
        experimental_compile: If false, execute the function in a regular way. The
          function is optimized by some graph rewrite passes (some ops might be
          clustered into a single op) and interpreted by the standard TensorFlow
          executor, which dispatches op kernels one by one as they become
          executable. Set it to false when directly running a multi-device
          function on TPUs (e.g. two TPU cores, one TPU core and its
          host CPU). If True, the function is compiled directly by XLA. XLA would
          fuse all the ops and emit more efficient code to run for some devices
          (e.g. TPU, XLA_GPU) and some use cases (e.g. dense tensor computation).
          It requires that the whole function is compilable by XLA. If None
          (default), compile the function with XLA when running on TPU and go
          through the regular function execution path when running on other
          devices.
  
      Raises:
        ValueError: if `input_signature` is not None and the `python_function`'s
          argspec has keyword arguments.
docstring_summary: Initializes a `Function`.
docstring_tokens: Initializes a Function .
function: def __init__(self,
                 python_function,
                 name,
                 input_signature=None,
                 autograph=True,
                 experimental_autograph_options=None,
                 experimental_relax_shapes=False,
                 experimental_compile=None):
      """Initializes a `Function`.
  
      Args:
        python_function: the function to be wrapped.
        name: the name given to it.
        input_signature: a possibly nested sequence of `TensorSpec` objects
          specifying the input signature of this function. If `None`, a separate
          function is instantiated for each inferred input signature.
        autograph: whether `python_function` should be converted to graph mode.
          See https://www.tensorflow.org/guide/autograph for more information.
        experimental_autograph_options: optional tuple of
          tensorflow.autograph.Feature values. Allows enabling additional
          conversion options when autograph is set to True.
        experimental_relax_shapes: When true, argument shapes may be relaxed to
          avoid unecessary retracing.
        experimental_compile: If false, execute the function in a regular way. The
          function is optimized by some graph rewrite passes (some ops might be
          clustered into a single op) and interpreted by the standard TensorFlow
          executor, which dispatches op kernels one by one as they become
          executable. Set it to false when directly running a multi-device
          function on TPUs (e.g. two TPU cores, one TPU core and its
          host CPU). If True, the function is compiled directly by XLA. XLA would
          fuse all the ops and emit more efficient code to run for some devices
          (e.g. TPU, XLA_GPU) and some use cases (e.g. dense tensor computation).
          It requires that the whole function is compilable by XLA. If None
          (default), compile the function with XLA when running on TPU and go
          through the regular function execution path when running on other
          devices.
  
      Raises:
        ValueError: if `input_signature` is not None and the `python_function`'s
          argspec has keyword arguments.
      """
      self._python_function = python_function
      self._function_spec = function_lib.FunctionSpec.from_function_and_signature(
          python_function, input_signature)
      self._autograph = autograph
      self._experimental_autograph_options = experimental_autograph_options
      self.experimental_relax_shapes = experimental_relax_shapes
      self._experimental_compile = experimental_compile
      self._created_variables = None
      self._stateful_fn = None
      self._stateless_fn = None
      self._descriptor_cache = weakref.WeakKeyDictionary()
      self._name = name
function_tokens: def __init__ ( self , python_function , name , input_signature = None , autograph = True , experimental_autograph_options = None , experimental_relax_shapes = False , experimental_compile = None ) : self . _python_function = python_function self . _function_spec = function_lib . FunctionSpec . from_function_and_signature ( python_function , input_signature ) self . _autograph = autograph self . _experimental_autograph_options = experimental_autograph_options self . experimental_relax_shapes = experimental_relax_shapes self . _experimental_compile = experimental_compile self . _created_variables = None self . _stateful_fn = None self . _stateless_fn = None self . _descriptor_cache = weakref . WeakKeyDictionary ( ) self . _name = name