def METHOD_NAME(self, x: paddle.Tensor, t: paddle.Tensor, c: paddle.Tensor):
    """Denoise mel-spectrogram.
    Args:
        x(Tensor): 
            Shape (B, C_in, T), The input mel-spectrogram.
        t(Tensor): 
            Shape (B), The timestep input.
        c(Tensor): 
            Shape (B, C_aux, T'). The auxiliary input (e.g. fastspeech2 encoder output).
    Returns:
        Tensor: Shape (B, C_out, T), the pred noise.
    """
    assert c.shape[-1] == x.shape[-1]
    if t.shape[0] != x.shape[0]:
        t = t.tile([x.shape[0]])
    t_emb = self.first_t_emb(t)
    t_embs = [
        t_emb_layer(t_emb)[..., None] for t_emb_layer in self.t_emb_layers
    ]
    x = self.first_conv(x)
    x = self.first_act(x)
    skips = 0
    for f, t in zip(self.conv_layers, t_embs):
        x = x + t
        x, s = f(x, c)
        skips += s
    skips *= math.sqrt(1.0 / len(self.conv_layers))
    x = self.last_conv_layers(skips)
    return x