class_docstr: Base class for graph functions.
  
  An `AtomicFunction` encapsulates a single graph function definition.
  
  `AtomicFunction` can be called directly only if no captures are needed
  according to the `FunctionType`. If captures are present, please use
  `call_with_captures` instead.
  
  `AtomicFunction` does not support gradients. Please use the parent
  `ConcreteFunction` if you need gradient support.
docstr: Calls this AtomicFunction with captures as defined by its FunctionType.
  
  Args:
    args: Tuple containing positional arguments
    kwargs: Dict containing keyword arguments
    captures: Tuple of tensors supplying captured tensor values.
  
  Returns:
    A structured output value based on the inputs.
docstr_tok: ["Calls","this","AtomicFunction","with","captures","as","defined","by","its","FunctionType",".","Args",":","args",":","Tuple","containing","positional","arguments","kwargs",":","Dict","containing","keyword","arguments","captures",":","Tuple","of","tensors","supplying","captured","tensor","values",".","Returns",":","A","structured","output","value","based","on","the","inputs","."]
code: def call_with_captures(self, args, kwargs, captures):
      """Calls this AtomicFunction with captures as defined by its FunctionType.
  
      Args:
        args: Tuple containing positional arguments
        kwargs: Dict containing keyword arguments
        captures: Tuple of tensors supplying captured tensor values.
  
      Returns:
        A structured output value based on the inputs.
      """
  
imports: import abc
  import inspect
  import sys
  import textwrap
  from typing import Union
  import numpy
  from tensorflow.python.types import doc_typealias
  from tensorflow.python import pywrap_tensorflow
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 for calling the AtomicFunction obj's underlying member method code with example usage: obj.call_with_captures(args, kwargs, captures) without return types