code: def ViTForImageClassification_pipeline_forward(
      stage_manager: PipelineStageManager, stage_index: List[int]
  ):
      from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
      from transformers.models.vit.modeling_vit import (
          ImageClassifierOutput,
      )
  
      def pp_forward(
          self,
          pixel_values: Optional[torch.Tensor] = None,
          head_mask: Optional[torch.Tensor] = None,
          labels: Optional[torch.Tensor] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          interpolate_pos_encoding: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          hidden_states: Optional[torch.FloatTensor] = None,
      ) -> Union[tuple, ImageClassifierOutput]:
          r"""
          labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
              Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
              config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
              `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
          """
          return_dict = (
              return_dict
              if return_dict is not None
              else self.config.use_return_dict
          )
  
          if not stage_manager.is_first_stage():
              assert (
                  hidden_states is not None
              ), f"Current stage is {stage_manager.stage}, hidden_states should not be None"
  
          outputs = self.vit(
              pixel_values,
              head_mask=head_mask,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              interpolate_pos_encoding=interpolate_pos_encoding,
              return_dict=return_dict,
              hidden_states=hidden_states,
          )
  
          # not last stage, return hidden_states
          if not stage_manager.is_last_stage():
              return outputs
          else:
              sequence_output = outputs[0]
  
          # last stage
          logits = self.classifier(sequence_output[:, 0, :])
  
          loss = None
          if labels is not None:
              # move labels to correct device to enable model parallelism
              labels = labels.to(logits.device)
              if self.config.problem_type is None:
                  if self.num_labels == 1:
                      self.config.problem_type = "regression"
                  elif self.num_labels > 1 and (
                      labels.dtype == torch.long
                      or labels.dtype == torch.int
                  ):
                      self.config.problem_type = (
                          "single_label_classification"
                      )
                  else:
                      self.config.problem_type = (
                          "multi_label_classification"
                      )
  
              if self.config.problem_type == "regression":
                  loss_fct = MSELoss()
                  if self.num_labels == 1:
                      loss = loss_fct(
                          logits.squeeze(), labels.squeeze()
                      )
                  else:
                      loss = loss_fct(logits, labels)
              elif (
                  self.config.problem_type
                  == "single_label_classification"
              ):
                  loss_fct = CrossEntropyLoss()
                  loss = loss_fct(
                      logits.view(-1, self.num_labels), labels.view(-1)
                  )
              elif (
                  self.config.problem_type
                  == "multi_label_classification"
              ):
                  loss_fct = BCEWithLogitsLoss()
                  loss = loss_fct(logits, labels)
  
          if not return_dict:
              output = (logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return ImageClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
      return pp_forward
  
file_path: colossalai/colossalai/shardformer/modeling/vit.py
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  from transformers.models.vit.modeling_vit import BaseModelOutput, ViTEncoder
  from transformers.utils import logging
  from colossalai.pipeline.stage_manager import PipelineStageManager
label_desc: Use image node_id 3 for calling a global function with example usage: ViTForImageClassification_pipeline_forward(stage_manager, stage_index) and returns: pp_forward, ImageClassifierOutput, outputs, unknown