class_docstr: A Python callable for functions in the TF Runtime.
  
  Provides core functionality for tf.function including:
    - automatic lifecycle management of runtime functions
    - structured inputs (including captures) and structured outputs
    - calls from both eager and graph mode
    - dependency tracking of children functions
    - runtime error interpolation to identify user code stack traces
    - control dependencies (including automatic)
docstr: Construct a new AtomicFunction.
  
  Args:
    name: str/bytes name of the runtime function in the bound context.
    bound_context: interface to the runtime for the AtomicFunction.
    function_type: input/output contract for the AtomicFunction
    children: list of AtomicFunctions that are needed to call this one.
    call_options: extra configuration options for the call.
    cached_graph: FuncGraph that this AtomicFunction was generated from (if
      known). Otherwise it will lazily construct a new corresponding FuncGraph
      if ever needed.
docstr_tok: ["Construct","a","new","AtomicFunction",".","Args",":","name",":","str\/bytes","name","of","the","runtime","function","in","the","bound","context",".","bound_context",":","interface","to","the","runtime","for","the","AtomicFunction",".","function_type",":","input\/output","contract","for","the","AtomicFunction","children",":","list","of","AtomicFunctions","that","are","needed","to","call","this","one",".","call_options",":","extra","configuration","options","for","the","call",".","cached_graph",":","FuncGraph","that","this","AtomicFunction","was","generated","from","(","if","known",")",".","Otherwise","it","will","lazily","construct","a","new","corresponding","FuncGraph","if","ever","needed","."]
code: def __init__(
      self,
      name: Union[str, bytes],
      bound_context: context.Context,
      function_type: function_type_lib.FunctionType,
      children: Optional[List["AtomicFunction"]] = None,
      call_options: CallOptions = CallOptions(),
      cached_graph: Optional[func_graph_module.FuncGraph] = None,
  ):
      """Construct a new AtomicFunction.
  
      Args:
        name: str/bytes name of the runtime function in the bound context.
        bound_context: interface to the runtime for the AtomicFunction.
        function_type: input/output contract for the AtomicFunction
        children: list of AtomicFunctions that are needed to call this one.
        call_options: extra configuration options for the call.
        cached_graph: FuncGraph that this AtomicFunction was generated from (if
          known). Otherwise it will lazily construct a new corresponding FuncGraph
          if ever needed.
      """
      self._name = compat.as_bytes(name)
      self._bound_context = bound_context
      self._function_type = function_type
      self._children = children if children else []
      self._call_options = call_options
      self._cached_definition = None
  
      self._cached_graph = cached_graph
      self._generated_graph = None
  
      ref_key = (self._bound_context.function_scope_id, self.name)
      if ref_key not in RUNTIME_FUNCTION_REFS:
          RUNTIME_FUNCTION_REFS[ref_key] = 1
      else:
          RUNTIME_FUNCTION_REFS[ref_key] += 1
  
imports: import dataclasses
  import traceback
  import typing
  from typing import Any, Dict, List, Optional, Sequence, Union
  from tensorflow.core.framework import attr_value_pb2
  from tensorflow.core.framework import function_pb2
  from tensorflow.core.framework import graph_debug_info_pb2
  from tensorflow.core.function.polymorphism import function_type
  from tensorflow.python.client import pywrap_tf_session
  from tensorflow.python.eager import context
  from tensorflow.python.eager import record
  from tensorflow.python.eager.polymorphic_function import attributes
  from tensorflow.python.eager.polymorphic_function import function_type_utils
  from tensorflow.python.framework import auto_control_deps_utils
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import error_interpolation
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import func_graph
  from tensorflow.python.framework import function_def_to_graph
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import handle_data_util
  from tensorflow.python.types import core
  from tensorflow.python.util import compat
  from tensorflow.python.util import function_utils
  from tensorflow.python.util import tf_stack
label_desc: Use image node_id 1 to create a new AtomicFunction object from inherited base classes: core with example: obj = AtomicFunction(name, bound_context, function_type, children, call_options, cached_graph)