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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 _mask(self, tensor, length, padding_value=0):
 """Set padding elements of a batch of sequences to a constant. Useful for setting padding elements to zero before summing along the time dimension, or for preventing infinite results in padding elements. Args: tensor: Tensor of sequences. length: Batch of sequence lengths. padding_value: Value to write into padding elements. Returns: Masked sequences. """

    with tf.name_scope('mask'):
      range_ = tf.range(tensor.shape[1].value)
      mask = range_[None, :] < length[:, None]
      if tensor.shape.ndims > 2:
        for _ in range(tensor.shape.ndims - 2):
          mask = mask[..., None]
        mask = tf.tile(mask, [1, 1] + tensor.shape[2:].as_list())
      masked = tf.where(mask, tensor, padding_value * tf.ones_like(tensor))
      return tf.check_numerics(masked, 'masked')