    def forward_feature(self, x: torch.tensor, layers: list = None):
        """

        Parameters
        ----------
        x : torch.tensor
            Input wavs. Shape should be (batch, channels, timesteps).
        layers : list[int], optional
            Layers of RVQ should return quantized result. The default is all layers.

        Returns
        -------
        quantized_list : list[torch.tensor]
            Quantized of required layers.

        """
        e = self.encoder(x)
        layers = layers if layers else list(range(self.n_q))
        quantized, codes, commit_loss, quantized_list = self.quantizer(e, layers=layers)
        return quantized_list
Parameters
----------
x : torch.tensor
Input wavs. Shape should be (batch, channels, timesteps).
layers : list[int], optional
Layers of RVQ should return quantized result. The default is all layers.

Returns
-------
quantized_list : list[torch.tensor]
Quantized of required layers.
models/codec/speechtokenizer/model.py