parameters: (self, query, processed_memory,
                                 attention_weights_cat)
return_statement: return energies
docstring: 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)
docstring_summary: 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)
docstring_tokens: 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 )
function: 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(2)
          return energies
function_tokens: def get_alignment_energies ( self , query , processed_memory , attention_weights_cat ) : 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 ( 2 ) return energies