(self, x_input, x_target, x_length, z=None,
                          c_input=None)
Reconstruction loss calculation.

    Args:
      x_input: Batch of decoder input sequences for teacher forcing, sized
          `[batch_size, max(x_length), output_depth]`.
      x_target: Batch of expected output sequences to compute loss against,
          sized `[batch_size, max(x_length), output_depth]`.
      x_length: Length of input/output sequences, sized `[batch_size]`.
      z: (Optional) Latent vectors. Required if model is conditional. Sized
          `[n, z_size]`.
      c_input: (Optional) Batch of control sequences, sized
          `[batch_size, max(x_length), control_depth]`. Required if conditioning
          on control sequences.

    Returns:
      r_loss: The reconstruction loss for each sequence in the batch.
      metric_map: Map from metric name to tf.metrics return values for logging.
Reconstruction loss calculation.
Reconstruction loss calculation .
def reconstruction_loss(self, x_input, x_target, x_length, z=None,
                          c_input=None):
    """Reconstruction loss calculation.

    Args:
      x_input: Batch of decoder input sequences for teacher forcing, sized
          `[batch_size, max(x_length), output_depth]`.
      x_target: Batch of expected output sequences to compute loss against,
          sized `[batch_size, max(x_length), output_depth]`.
      x_length: Length of input/output sequences, sized `[batch_size]`.
      z: (Optional) Latent vectors. Required if model is conditional. Sized
          `[n, z_size]`.
      c_input: (Optional) Batch of control sequences, sized
          `[batch_size, max(x_length), control_depth]`. Required if conditioning
          on control sequences.

    Returns:
      r_loss: The reconstruction loss for each sequence in the batch.
      metric_map: Map from metric name to tf.metrics return values for logging.
    """
    pass
def reconstruction_loss ( self , x_input , x_target , x_length , z = None , c_input = None ) : pass