class_docstr: Base class for differentiable graph functions.
  
  A `ConcreteFunction` encapsulates the original graph function definition with
  support for differentiability under `tf.GradientTape` contexts. In the
  process, it may generate new graph functions (using the original) to
  efficiently perform forwards and backwards passes.
docstr: Returns the original `AtomicFunction` owned by this ConcreteFunction.
docstr_tok: ["Returns","the","original","`","AtomicFunction","`","owned","by","this","ConcreteFunction","."]
code: def inference_fn(self) -> AtomicFunction:
      """Returns the original `AtomicFunction` owned by this ConcreteFunction."""
  
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 ConcreteFunction obj's underlying member method code with example usage: obj.inference_fn() without return types