class_docstr: Base class for all TensorFlow decorators.
  
  TFDecorator captures and exposes the wrapped target, and provides details
  about the current decorator.
class_docstr_tok: ["Base","class","for","all","TensorFlow","decorators",".","TFDecorator","captures","and","exposes","the","wrapped","target",",","and","provides","details","about","the","current","decorator","."]
code: def __init__(
      self,
      decorator_name,
      target,
      decorator_doc="",
      decorator_argspec=None,
  ):
      self._decorated_target = target
      self._decorator_name = decorator_name
      self._decorator_doc = decorator_doc
      self._decorator_argspec = decorator_argspec
      if hasattr(target, "__name__"):
          self.__name__ = target.__name__
      if hasattr(target, "__qualname__"):
          self.__qualname__ = target.__qualname__
      if self._decorator_doc:
          self.__doc__ = self._decorator_doc
      elif hasattr(target, "__doc__") and target.__doc__:
          self.__doc__ = target.__doc__
      else:
          self.__doc__ = ""
  
      if decorator_argspec:
          self.__signature__ = fullargspec_to_signature(
              decorator_argspec
          )
      elif callable(target):
          try:
              self.__signature__ = inspect.signature(target)
          except (TypeError, ValueError):
              # Certain callables such as builtins can not be inspected for signature.
              pass
  
file_path: tensorflow/tensorflow/python/util/tf_decorator.py
imports: import inspect
  from typing import Dict, Any
label_desc: Use image node_id 1 to create a new TFDecorator object from inherited base classes: object with example: obj = TFDecorator(decorator_name, target, decorator_doc, decorator_argspec)