code: def forward(
      self, sample_list: Dict[str, Tensor]
  ) -> Dict[str, Tensor]:
      text_embedding = self.text_embeddings(
          sample_list["input_ids"], sample_list["segment_ids"]
      )
      image_embedding = self.image_embeddings(sample_list["image"])
      self.preprocess_sample(sample_list, image_embedding)
  
      # Feed through encoder
      embeddings = torch.cat([text_embedding, image_embedding], dim=1)
      attention_mask = self.get_attention_mask(
          sample_list, text_embedding, image_embedding
      )
      sequence, _ = self.encoder(
          embeddings, attention_mask=attention_mask
      )
      if sequence.dim() != 3:
          sequence = sequence.unsqueeze(1)
  
      outputs = self.heads_dict(
          sample_list["dataset_name"], sequence, sample_list
      )
      return outputs
  
imports: import collections.abc
  import logging
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.models.transformers.heads.utils import build_heads_dict
  from mmf.modules.encoders import TransformerEncoder, ViTEncoder
  from mmf.modules.losses import MMFLoss
  from mmf.utils.build import build_encoder
  from mmf.utils.modeling import get_bert_configured_parameters
  from omegaconf import MISSING, OmegaConf
  from torch import nn, Tensor
label_desc: Use image node_id 4 for calling the ViLT obj's underlying member method code with example usage: obj.forward(sample_list) and returns: outputs