def get_alignment_energies(self, query, processed_memory,
                               attention_weights_cat):
        """
        PARAMS
        ------
        query: decoder output (batch, n_mel_channels * n_frames_per_step)
        processed_memory: processed encoder outputs (B, T_in, attention_dim)
        attention_weights_cat: cumulative and prev. att weights (B, 2, max_time)

        RETURNS
        -------
        alignment (batch, max_time)
        """

        processed_query = self.query_layer(query.unsqueeze(1))
        processed_attention_weights = self.location_layer(attention_weights_cat)
        energies = self.v(torch.tanh(
            processed_query + processed_attention_weights + processed_memory))

        energies = energies.squeeze(-1)
        return energies

        PARAMS
        ------
        query: decoder output (batch, n_mel_channels * n_frames_per_step)
        processed_memory: processed encoder outputs (B, T_in, attention_dim)
        attention_weights_cat: cumulative and prev. att weights (B, 2, max_time)

        RETURNS
        -------
        alignment (batch, max_time)
        
structured_prompting/fairseq-version/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py