Merge branch 'numpy_seed' into 'main'

        sample = [self.indexed_dataset[i] for i in range(start_idx, end_idx)]

        # Note that this rng state should be numpy and not python since

        # python randint is inclusive whereas the numpy one is exclusive.

        np_rng = np.random.RandomState(seed=(self.seed + idx))

        # We % 2**32 since numpy requres the seed to be between 0 and 2**32 - 1

        np_rng = np.random.RandomState(seed=((self.seed + idx) % 2**32))

        return build_training_sample(sample, seq_length,

                                     self.max_seq_length,  # needed for padding

                                     self.vocab_id_list,