code: def forward(
      self,
      x: Tensor,
      attention_mask: Optional[Tensor] = None,
      position_bias: Optional[Tensor] = None,
  ) -> Tensor:
      x = self._preprocess(x)
      for layer in self.layers:
          if not (
              self.training and torch.rand(1).item() <= self.layer_drop
          ):
              x, position_bias = layer(
                  x, attention_mask, position_bias=position_bias
              )
  
      if not self.layer_norm_first:
          x = self.layer_norm(x)
      return x
  
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 3 for calling the Transformer obj's underlying member method code with example usage: obj.forward(x, attention_mask, position_bias) and returns: x