code: def get_intermediate_outputs(
      self,
      x: Tensor,
      attention_mask: Optional[Tensor] = None,
      num_layers: Optional[int] = None,
  ) -> List[Tensor]:
      if num_layers is not None:
          if not 0 < num_layers <= len(self.layers):
              raise ValueError(
                  f"`num_layers` must be between [1, {len(self.layers)}]"
              )
  
      ret: List[Tensor] = []
      position_bias = None
      x = self._preprocess(x)
      for layer in self.layers:
          x, position_bias = layer(
              x, attention_mask, position_bias=position_bias
          )
          ret.append(x)
          if num_layers is not None and len(ret) >= num_layers:
              return ret
      return ret
  
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 4 for calling the Transformer obj's underlying member method code with example usage: obj.get_intermediate_outputs(x, attention_mask, num_layers) and returns: ret, ret