(self, input)
return output
input: (samples_num, freq_bins, time_steps, 1)
input: (samples_num, freq_bins, time_steps, 1)
input : ( samples_num freq_bins time_steps 1 )
def forward(self, input):
        """input: (samples_num, freq_bins, time_steps, 1)
        """

        # (samples_num, hidden_units, time_steps, 1)
        b1 = self.emb(input)

        # (samples_num, time_steps, hidden_units)
        b1 = b1[:, :, :, 0].transpose(1, 2)

        b2 = F.sigmoid(self.fc_final(b1))

        # (samples_num, classes_num)
        output = torch.mean(b2, dim=1)

        return output
def forward ( self , input ) : # (samples_num, hidden_units, time_steps, 1) b1 = self . emb ( input ) # (samples_num, time_steps, hidden_units) b1 = b1 [ : , : , : , 0 ] . transpose ( 1 , 2 ) b2 = F . sigmoid ( self . fc_final ( b1 ) ) # (samples_num, classes_num) output = torch . mean ( b2 , dim = 1 ) return output