code: def forward(
      self,
      hidden_states: torch.Tensor,
      head_mask: Optional[torch.Tensor] = None,
      output_attentions: bool = False,
      output_hidden_states: bool = False,
      return_dict: bool = True,
  ) -> Union[tuple, BaseModelOutput]:
      all_hidden_states = () if output_hidden_states else None
      all_self_attentions = () if output_attentions else None
  
      for i, layer_module in enumerate(self.layer):
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          layer_head_mask = (
              head_mask[i] if head_mask is not None else None
          )
  
          if self.gradient_checkpointing and self.training:
              layer_outputs = self._gradient_checkpointing_func(
                  layer_module.__call__,
                  hidden_states,
                  layer_head_mask,
                  output_attentions,
              )
          else:
              layer_outputs = layer_module(
                  hidden_states, layer_head_mask, output_attentions
              )
  
          hidden_states = layer_outputs[0]
  
          if output_attentions:
              all_self_attentions = all_self_attentions + (
                  layer_outputs[1],
              )
  
      if output_hidden_states:
          all_hidden_states = all_hidden_states + (hidden_states,)
  
      if not return_dict:
          return tuple(
              v
              for v in [
                  hidden_states,
                  all_hidden_states,
                  all_self_attentions,
              ]
              if v is not None
          )
      return BaseModelOutput(
          last_hidden_state=hidden_states,
          hidden_states=all_hidden_states,
          attentions=all_self_attentions,
      )
  
imports: import collections.abc
  import math
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from ...activations import ACT2FN
  from ...modeling_outputs import BackboneOutput, BaseModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from ...utils.backbone_utils import BackboneMixin
  from .configuration_vitdet import VitDetConfig
label_desc: Use image node_id 2 for calling the VitDetEncoder obj's underlying member method code with example usage: obj.forward(hidden_states, head_mask, output_attentions, output_hidden_states, return_dict) and returns: BaseModelOutput, tuple