def prior_preprocess(self, tokens, conds):
        """
        Shifts the input tokens to account for the dictionary merge. The embed_dim_shift give by how much the music
        tokens should be shifted by. It is equal to `lyric_vocab_size`.
        """
        batch_size = tokens[0].shape[0]
        for i in range(len(tokens)):
            tokens[i] = (tokens[i] + int(self.embed_dim_shift[i])).view(batch_size, -1)

        for i in range(len(conds)):
            if conds[i] is None:
                conds[i] = torch.zeros(
                    (batch_size, self.input_shapes[i], self.width), dtype=tokens[0].dtype, device=tokens[0].device
                )

        return torch.cat(tokens, dim=1), torch.cat(conds, dim=1)

        Shifts the input tokens to account for the dictionary merge. The embed_dim_shift give by how much the music
        tokens should be shifted by. It is equal to `lyric_vocab_size`.
        
dpkd/transformers/src/transformers/models/jukebox/modeling_jukebox.py