code: def _preprocess(
      self,
      features: Tensor,
      lengths: Optional[Tensor] = None,
  ) -> Tuple[Tensor, Optional[Tensor]]:
      x = self.feature_projection(features)
  
      mask: Optional[Tensor] = None
      if lengths is not None:
          batch_size, max_len, _ = x.shape
          # create mask for padded elements and zero-out them
          mask = (
              torch.arange(max_len, device=lengths.device).expand(
                  batch_size, max_len
              )
              >= lengths[:, None]
          )
          x[mask] = 0.0
          # extend the mask to attention shape and set weight
          mask = -10000.0 * mask[:, None, None, :].to(
              dtype=features.dtype
          )
          mask = mask.expand(batch_size, 1, max_len, max_len)
      return x, mask
  
imports: import logging
  from typing import List, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torch.nn import Module, Parameter
  from .wavlm_attention import WavLMSelfAttention
label_desc: Use image node_id 2 for calling the Encoder obj's underlying member method code with example usage: obj._preprocess(features, lengths) and returns: x,  mask