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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 stop_gradient(cls, x: 'TensorFluent') -> 'TensorFluent':
        '''Returns a copy of the input fluent with stop_gradient at tensor level.

        Args:
            x: The input fluent.

        Returns:
            A TensorFluent that stops backpropagation of gradient computations.
        '''
        scope = x.scope.as_list()
        batch = x.batch
        return TensorFluent(tf.stop_gradient(x.tensor), scope, batch)