code: def forward(
      self, hidden_states: torch.Tensor, input_tensor: torch.Tensor
  ) -> torch.Tensor:
      hidden_states = self.dense(hidden_states)
      hidden_states = self.dropout(hidden_states)
      hidden_states = self.output_adapters.bottleneck_layer_forward(
          hidden_states, input_tensor, None
      )
  
      return hidden_states
  
file_path: adapters/src/adapters/models/vit/modeling_vit.py
imports: import math
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from adapters.composition import adjust_tensors_for_parallel, match_attn_matrices_for_parallel
  from transformers.models.vit.modeling_vit import ViTLayer, ViTOutput, ViTSelfAttention
  from .mixin_vit import ViTLayerAdaptersMixin, ViTOutputAdaptersMixin, ViTSelfAttentionAdaptersMixin
label_desc: Use image node_id 1 for calling the ViTOutputWithAdapters obj's underlying member method code with example usage: obj.forward(hidden_states, input_tensor) and returns: hidden_states