parameters: (self, step, output)
return_statement: return False
docstring: Callback after training step finished.
  
      This callback provides access to the tensors/ops evaluated at this step,
      including the additional tensors for which evaluation was requested in
      `step_begin`.
  
      In addition, the callback has the opportunity to stop training by returning
      `True`. This is useful for early stopping, for example.
  
      Note that this method is not called if the call to `Session.run()` that
      followed the last call to `step_begin()` failed.
  
      Args:
        step: `int`, the current value of the global step.
        output: `dict` mapping `string` values representing tensor names to
          the value resulted from running these tensors. Values may be either
          scalars, for scalar tensors, or Numpy `array`, for non-scalar tensors.
  
      Returns:
        `bool`. True if training should stop.
  
      Raises:
        ValueError: if we've not begun a step, or `step` number does not match.
docstring_summary: Callback after training step finished.
docstring_tokens: Callback after training step finished .
function: def step_end(self, step, output):  # pylint: disable=unused-argument
      """Callback after training step finished.
  
      This callback provides access to the tensors/ops evaluated at this step,
      including the additional tensors for which evaluation was requested in
      `step_begin`.
  
      In addition, the callback has the opportunity to stop training by returning
      `True`. This is useful for early stopping, for example.
  
      Note that this method is not called if the call to `Session.run()` that
      followed the last call to `step_begin()` failed.
  
      Args:
        step: `int`, the current value of the global step.
        output: `dict` mapping `string` values representing tensor names to
          the value resulted from running these tensors. Values may be either
          scalars, for scalar tensors, or Numpy `array`, for non-scalar tensors.
  
      Returns:
        `bool`. True if training should stop.
  
      Raises:
        ValueError: if we've not begun a step, or `step` number does not match.
      """
      if self._current_step != step:
        raise ValueError(
            "step_end expected %s but got %s.", self._current_step, step)
      self._current_step = None
      return False
function_tokens: def step_end ( self , step , output ) : # pylint: disable=unused-argument if self . _current_step != step : raise ValueError ( "step_end expected %s but got %s." , self . _current_step , step ) self . _current_step = None return False