code: def forward(self, hidden_states, input_tensor):
      hidden_states = self.dense(hidden_states)
      hidden_states = self.dropout(hidden_states)
      hidden_states = self.bottleneck_layer_forward(
          hidden_states, input_tensor, self.LayerNorm
      )
      return hidden_states
  
file_path: adapters/src/adapters/models/deberta_v2/modeling_deberta_v2.py
imports: import torch
  import torch.utils.checkpoint
  from transformers.models.deberta_v2.modeling_deberta_v2 import DebertaV2Output, DebertaV2SelfOutput, DisentangledSelfAttention, XSoftmax
  from ...composition import adjust_tensors_for_parallel, match_attn_matrices_for_parallel
  from ...utils import prefix_attention_mask
  from ..bert.mixin_bert import BertOutputAdaptersMixin, BertSelfOutputAdaptersMixin
  from .mixin_deberta_v2 import DebertaV2SelfAttentionAdaptersMixin
label_desc: Use image node_id 1 for calling the DebertaV2OutputWithAdapters obj's underlying member method code with example usage: obj.forward(hidden_states, input_tensor) and returns: hidden_states