def __init__(self, inputs, sequence_length, mixing_concentration=1.,
               time_major=False, seed=None, scheduling_seed=None, name=None):
    """Initializer.

    Args:
      inputs: A (structure of) input tensors.
      sequence_length: An int32 vector tensor.
      mixing_concentration: <float32> [] for the alpha parameter of the
        Dirichlet distribution used to sample mixing weights from [0, 1].
      time_major: Python bool. Whether the tensors in `inputs` are time major.
        If `False` (default), they are assumed to be batch major.
      seed: The sampling seed.
      scheduling_seed: The schedule decision rule sampling seed.
      name: Name scope for any created operations.

    Raises:
      ValueError: if `sampling_probability` is not a scalar or vector.
    """
    with tf.name_scope(name, "ScheduledContinuousEmbedding",
                       [mixing_concentration]):
      self._mixing_concentration = tf.convert_to_tensor(
          mixing_concentration, name="mixing_concentration")
      if self._mixing_concentration.get_shape().ndims == 0:
        self._mixing_concentration = tf.expand_dims(self._mixing_concentration,
                                                    0)
      if (self._mixing_concentration.get_shape().ndims != 1 or
          self._mixing_concentration.get_shape().as_list()[0] > 1):
        raise ValueError(
            "mixing_concentration must be a scalar. saw shape: %s" %
            (self._mixing_concentration.get_shape()))
      self._seed = seed
      self._scheduling_seed = scheduling_seed
      super(ScheduledContinuousEmbeddingTrainingHelper, self).__init__(
          inputs=inputs,
          sequence_length=sequence_length,
          time_major=time_major,
          name=name)
Initializer.

    Args:
      inputs: A (structure of) input tensors.
      sequence_length: An int32 vector tensor.
      mixing_concentration: <float32> [] for the alpha parameter of the
        Dirichlet distribution used to sample mixing weights from [0, 1].
      time_major: Python bool. Whether the tensors in `inputs` are time major.
        If `False` (default), they are assumed to be batch major.
      seed: The sampling seed.
      scheduling_seed: The schedule decision rule sampling seed.
      name: Name scope for any created operations.

    Raises:
      ValueError: if `sampling_probability` is not a scalar or vector.
    
language/labs/consistent_zero_shot_nmt/modules/helpers.py