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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 Uniform(cls,
            low: 'TensorFluent', high: 'TensorFluent',
            batch_size: Optional[int] = None) -> Tuple[Distribution, 'TensorFluent']:
        '''Returns a TensorFluent for the Uniform sampling op with given low and high parameters.

        Args:
            low: The low parameter of the Uniform distribution.
            high: The high parameter of the Uniform distribution.
            batch_size: The size of the batch (optional).

        Returns:
            The Uniform distribution and a TensorFluent sample drawn from the distribution.

        Raises:
            ValueError: If parameters do not have the same scope.
        '''
        if low.scope != high.scope:
            raise ValueError('Uniform distribution: parameters must have same scope!')
        dist = tf.distributions.Uniform(low.tensor, high.tensor)
        batch = low.batch or high.batch
        if not batch and batch_size is not None:
            t = dist.sample(batch_size)
            batch = True
        else:
            t = dist.sample()
        scope = low.scope.as_list()
        return (dist, TensorFluent(t, scope, batch=batch))