class_docstr: Dispatcher that handles op if any arguments have a specified type.
  
  Checks the types of the arguments and keyword arguments (including elements
  of lists or tuples), and if any argument values have the indicated type(s),
  then delegates to an override function.
class_docstr_tok: ["Dispatcher","that","handles","op","if","any","arguments","have","a","specified","type",".","Checks","the","types","of","the","arguments","and","keyword","arguments","(","including","elements","of","lists","or","tuples",")",",","and","if","any","argument","values","have","the","indicated","type","(","s",")",",","then","delegates","to","an","override","function","."]
code: def handle(self, args, kwargs):
      if self._handles(args, kwargs):
          return self._override_func(*args, **kwargs)
      else:
          return self.NOT_SUPPORTED
  
imports: import collections
  import itertools
  import typing
  from tensorflow.python.framework import _pywrap_python_api_dispatcher
  from tensorflow.python.framework import ops
  from tensorflow.python.util import tf_decorator
  from tensorflow.python.util import tf_export
  from tensorflow.python.util import tf_inspect
  from tensorflow.python.util import traceback_utils
  from tensorflow.python.util import type_annotations
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 3 for calling the _TypeBasedDispatcher obj's underlying member method code with example usage: obj.handle(args, kwargs) and returns: self, self