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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_logging_tensor_hook(every_n_iter=100, tensors_to_log=None, **kwargs):
 # pylint: disable=unused-argument """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. """

  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)