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.bottleneck_layer_forward(
          hidden_states, input_tensor, self.LayerNorm
      )
      return hidden_states
  
file_path: adapters/src/adapters/models/electra/modeling_electra.py
imports: import math
  from typing import Optional, Tuple
  import torch
  from torch import nn
  from transformers.models.electra.modeling_electra import ElectraOutput, ElectraSelfAttention, ElectraSelfOutput
  from ...composition import adjust_tensors_for_parallel, match_attn_matrices_for_parallel
  from ...utils import prefix_attention_mask
  from ..bert.mixin_bert import BertOutputAdaptersMixin, BertSelfAttentionAdaptersMixin, BertSelfOutputAdaptersMixin
label_desc: Use image node_id 1 for calling the ElectraOutputWithAdapters obj's underlying member method code with example usage: obj.forward(hidden_states, input_tensor) and returns: hidden_states