code: def forward(self, hidden_states, input_tensor):
      hidden_states = self.dense(hidden_states)
  
      hidden_states = self.dropout(hidden_states)
  
      hidden_states = hidden_states + input_tensor
  
      return hidden_states
  
imports: import math
  from typing import Optional, Set, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, 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_vivit import VivitConfig
label_desc: Use image node_id 2 for calling the VivitOutput obj's underlying member method code with example usage: obj.forward(hidden_states, input_tensor) and returns: hidden_states