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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 sample_frame_single_env(self, batch_size, forward_steps=1):
 """ Return an in index of a random set of frames from a buffer, that have enough history and future """

        # Whole idea of this function is to make sure that sample we take is far away from the point which we are
        # currently writing to the buffer, which is 'discontinuous'

        if self.current_size < self.buffer_capacity:
            # Sample from up to total size of the buffer
            # -1 because we cannot take the last one
            return np.random.choice(self.current_size - forward_steps, batch_size, replace=False)
        else:
            candidate = np.random.choice(self.buffer_capacity, batch_size, replace=False)

            forbidden_ones = (
                np.arange(self.current_idx - forward_steps + 1, self.current_idx + self.frame_history)
                % self.buffer_capacity
            )

            # Exclude these frames for learning as they may have some part of history overwritten
            while any(x in candidate for x in forbidden_ones):
                candidate = np.random.choice(self.buffer_capacity, batch_size, replace=False)

            return candidate