docstr: Forward pass through the adapter layer.
  
  Args:
      hidden_states (torch.Tensor): Input hidden states to the adapter layer.
      residual_input (torch.Tensor): Residual input to the adapter layer.
      layer_norm (torch.nn.Module): Transformer layer normalization module to be used by the adapter layer.
  
  Returns:
      torch.Tensor: Output hidden states of the adapter layer.
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code: def forward(self, hidden_states, residual_input, layer_norm):
      """Forward pass through the adapter layer.
  
      Args:
          hidden_states (torch.Tensor): Input hidden states to the adapter layer.
          residual_input (torch.Tensor): Residual input to the adapter layer.
          layer_norm (torch.nn.Module): Transformer layer normalization module to be used by the adapter layer.
  
      Returns:
          torch.Tensor: Output hidden states of the adapter layer.
      """
      return self.bottleneck_layer_forward(
          hidden_states, residual_input, layer_norm
      )
  
imports: from typing import Dict, List, Mapping, NamedTuple, Optional, Union
  import torch
  from torch import nn
  from ..composition import AdapterCompositionBlock, Average, BatchSplit, Fuse, Parallel, Split, Stack, adjust_tensors_for_parallel
  from ..configuration import BnConfig
  from ..context import ForwardContext
  from .adapter_layer_base import ComposableAdapterLayerBase
  from .modeling import Adapter, BertFusion, ParallelAdapter
label_desc: Use image node_id 20 for calling the BottleneckLayer obj's underlying member method code with example usage: obj.forward(hidden_states, residual_input, layer_norm) and returns: self