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 = hidden_states + input_tensor
  
      return hidden_states
  
imports: import collections.abc
  import math
  from typing import Dict, List, Optional, Set, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_vit_msn import ViTMSNConfig
label_desc: Use image node_id 2 for calling the ViTMSNOutput obj's underlying member method code with example usage: obj.forward(hidden_states, input_tensor) and returns: hidden_states