parameters: (self, step, outputs)
return_statement: return False
docstring: Callback after every n'th 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.
  
      Args:
        step: `int`, the current value of the global step.
        outputs: `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.
docstring_summary: Callback after every n'th step finished.
docstring_tokens: Callback after every n th step finished .
function: def every_n_step_end(self, step, outputs):  # pylint: disable=unused-argument
      """Callback after every n'th 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.
  
      Args:
        step: `int`, the current value of the global step.
        outputs: `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.
      """
      return False
function_tokens: def every_n_step_end ( self , step , outputs ) : # pylint: disable=unused-argument return False