docs: def Normal(cls,
              mean: 'TensorFluent', variance: 'TensorFluent',
              batch_size: Optional[int] = None) -> Tuple[Distribution, 'TensorFluent']:
          '''Returns a TensorFluent for the Normal sampling op with given mean and variance.
  
          Args:
              mean: The mean parameter of the Normal distribution.
              variance: The variance parameter of the Normal distribution.
              batch_size: The size of the batch (optional).
  
          Returns:
              The Normal distribution and a TensorFluent sample drawn from the distribution.
  
          Raises:
              ValueError: If parameters do not have the same scope.
          '''
          if mean.scope != variance.scope:
              raise ValueError('Normal distribution: parameters must have same scope!')
          loc = mean.tensor
          scale = tf.sqrt(variance.tensor)
          dist = tf.distributions.Normal(loc, scale)
          batch = mean.batch or variance.batch
          if not batch and batch_size is not None:
              t = dist.sample(batch_size)
              batch = True
          else:
              t = dist.sample()
          scope = mean.scope.as_list()
          return (dist, TensorFluent(t, scope, batch=batch))