code: def forward(
      self,
      hidden_states,
      attention_mask=None,
      head_mask=None,
      output_attentions=False,
      output_hidden_states=False,
      return_dict=True,
  ):
      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 (
              getattr(self.config, "gradient_checkpointing", False)
              and self.training
          ):
  
              def create_custom_forward(module):
                  def custom_forward(*inputs):
                      return module(*inputs, output_attentions)
  
                  return custom_forward
  
              layer_outputs = torch.utils.checkpoint.checkpoint(
                  create_custom_forward(layer_module),
                  hidden_states,
                  attention_mask,
                  layer_head_mask,
                  use_reentrant=True,
              )
          else:
              layer_outputs = layer_module(
                  hidden_states,
                  attention_mask,
                  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 vit.BaseModelOutput(
          last_hidden_state=hidden_states,
          hidden_states=all_hidden_states,
          attentions=all_self_attentions,
      )
  
imports: from collections import namedtuple
  from dataclasses import asdict, dataclass
  import torch
  from mmf.utils.general import retry_n
  from omegaconf import OmegaConf
  from packaging import version
  from torch import nn
label_desc: Use image node_id 2 for calling the ViTEncoder obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, head_mask, output_attentions, output_hidden_states, return_dict) and returns: vit, tuple, custom_forward, module