class_docstr: An object holding the states surrounding the dumping callback.
class_docstr_tok: ["An","object","holding","the","states","surrounding","the","dumping","callback","."]
docstr: Attempt to get the ID of a FuncGraph based on an op type name.
  
  Also caches the ID for faster access later.
  
  Args:
    op_type: Op type string, which may be the name of a function.
  
  Returns:
    If the op_type name does not fit the pattern of a function name (e.g.,
    one that starts with "__inference_"), `None` is returned immediately.
    Else, if the FuncGraph is found, ID of the underlying FuncGraph is
    returned as a string.
    Else, `None` is returned.
code: def _func_graph_id_from_func_name(self, op_type):
      """Attempt to get the ID of a FuncGraph based on an op type name.
  
      Also caches the ID for faster access later.
  
      Args:
        op_type: Op type string, which may be the name of a function.
  
      Returns:
        If the op_type name does not fit the pattern of a function name (e.g.,
        one that starts with "__inference_"), `None` is returned immediately.
        Else, if the FuncGraph is found, ID of the underlying FuncGraph is
        returned as a string.
        Else, `None` is returned.
      """
      op_type = compat.as_bytes(op_type)
      if is_op_type_function(op_type):
          # op_type for eagerly-executed FuncGraphs have the prefixed and suffixed
          # form such as "__inference_my_function_13579", wherein the middle part
          # "my_function" is the name of the Python function from which the
          # FuncGraph is compiled. Due to the suffix, the op_type is unique for
          # - duplicate Python function names
          # - multiple compilation of the same Python function
          if op_type in self._op_type_to_context_id:
              return self._op_type_to_context_id[op_type]
          with self._context_lock:
              for function in self._function_to_graph_id:
                  if function.name == op_type:
                      graph_id = self._function_to_graph_id[function]
                      self._op_type_to_context_id[op_type] = graph_id
                      return graph_id
          return None
      else:
          return None
  
imports: import atexit
  import os
  import re
  import socket
  import threading
  import uuid
  from tensorflow.core.framework import graph_debug_info_pb2
  from tensorflow.core.framework import tensor_pb2
  from tensorflow.core.protobuf import debug_event_pb2
  from tensorflow.python.debug.lib import debug_events_writer
  from tensorflow.python.debug.lib import op_callbacks_common
  from tensorflow.python.debug.lib import source_utils
  from tensorflow.python.eager import function
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import op_callbacks
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_debug_ops
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import compat
  from tensorflow.python.util import object_identity
  from tensorflow.python.util import tf_stack
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 18 for calling the _DumpingCallback obj's underlying member method code with example usage: obj._func_graph_id_from_func_name(op_type) and returns: None, None, self, graph_id