code: def forward(self, hidden_states):
      # We "pool" the model by simply taking the hidden state corresponding
      # to the first token.
      first_token_tensor = hidden_states[:, 0]
      pooled_output = self.dense(first_token_tensor)
      pooled_output = self.activation(pooled_output)
      return pooled_output
  
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 VivitPooler obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: pooled_output