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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 Exponential(cls,
            mean: 'TensorFluent',
            batch_size: Optional[int] = None) -> Tuple[Distribution, 'TensorFluent']:
        '''Returns a TensorFluent for the Exponential sampling op with given mean parameter.

        Args:
            mean: The mean parameter of the Exponential distribution.
            batch_size: The size of the batch (optional).

        Returns:
            The Exponential distribution and a TensorFluent sample drawn from the distribution.
        '''
        rate = 1 / mean.tensor
        dist = tf.distributions.Exponential(rate)
        batch = mean.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))