class_docstr: VINVL wrapper for classification
code: def forward(
      self,
      input_ids: Tensor,
      token_type_ids: Tensor,
      attention_mask: Tensor,
      img_feats: Tensor,
      position_ids: Optional[Tensor] = None,
      labels: Optional[Tensor] = None,
  ) -> Dict[str, Tensor]:
      sequence_output = self.bert(
          input_ids,
          img_feats=img_feats,
          position_ids=position_ids,
          token_type_ids=token_type_ids,
          attention_mask=attention_mask,
      ).last_hidden_state
      logits = self.classifier(sequence_output)["scores"]
      result = {"scores": logits}
  
      if labels is not None:
          ce_loss = self.ce_loss(
              logits.view(-1, logits.size(1)), labels.view(-1)
          )
          result["losses"] = {"ce": ce_loss}
      return result
  
imports: import logging
  from collections import namedtuple
  from dataclasses import asdict, dataclass
  from typing import Any, Dict, Optional, Tuple
  import torch
  from mmf.common.registry import registry
  from mmf.common.sample import SampleList
  from mmf.models.base_model import BaseModel
  from mmf.models.transformers.heads.contrastive import ThreeWayContrastive
  from mmf.models.transformers.heads.mlm import MLM
  from mmf.models.transformers.heads.mlp import MLP
  from mmf.utils.general import retry_n
  from omegaconf import MISSING, OmegaConf
  from torch import nn, Tensor
label_desc: Use image node_id 2 for calling the VinVLForClassification obj's underlying member method code with example usage: obj.forward(input_ids, token_type_ids, attention_mask, img_feats, position_ids, labels) and returns: result