def get_energy_embedding(self, x, target, mask, control):
        x.detach() + self.predictor_grad * (x - x.detach())
        prediction = self.energy_predictor(x, squeeze=True)
        if target is not None:
            embedding = self.energy_embedding(torch.bucketize(target, self.energy_bins))
        else:
            prediction = prediction * control
            embedding = self.energy_embedding(
                torch.bucketize(prediction, self.energy_bins)
            )
        return prediction, embedding

def get_pitch_embedding(self, decoder_inp, f0, uv, mel2ph, control, encoder_out=None):
        pitch_pred = f0_denorm = cwt = f0_mean = f0_std = None
        if self.pitch_type == "ph":
            pitch_pred_inp = encoder_out.detach() + self.predictor_grad * (encoder_out - encoder_out.detach())
            pitch_padding = encoder_out.sum().abs() == 0
            pitch_pred = self.pitch_predictor(pitch_pred_inp) * control
            if f0 is None:
                f0 = pitch_pred[:, :, 0]
            f0_denorm = denorm_f0(f0, None, self.preprocess_config["preprocessing"]["pitch"], pitch_padding=pitch_padding)
            pitch = f0_to_coarse(f0_denorm)  # start from 0 [B, T_txt]
            pitch = F.pad(pitch, [1, 0])
            pitch = torch.gather(pitch, 1, mel2ph)  # [B, T_mel]
            pitch_embed = self.pitch_embed(pitch)