code: def get_logging_tensor_hook(every_n_iter=100, tensors_to_log=None, **kwargs):  # pylint: disable=unused-argument
    
    if tensors_to_log is None:
      tensors_to_log = _TENSORS_TO_LOG
  
    return tf.train.LoggingTensorHook(
        tensors=tensors_to_log,
        every_n_iter=every_n_iter)
docs: Function to get LoggingTensorHook.
  
    Args:
      every_n_iter: `int`, print the values of `tensors` once every N local
        steps taken on the current worker.
      tensors_to_log: List of tensor names or dictionary mapping labels to tensor
        names. If not set, log _TENSORS_TO_LOG by default.
      **kwargs: a dictionary of arguments to LoggingTensorHook.
  
    Returns:
      Returns a LoggingTensorHook with a standard set of tensors that will be
      printed to stdout.