code: def forward(
      self, sample_list: Dict[str, Any]
  ) -> Dict[str, torch.Tensor]:
      sample_list.text_mask = sample_list.text.eq(0)
      sample_list.text = self.word_embedding(sample_list.text)
      (
          text_embedding_total,
          text_embedding_vec,
      ) = self.process_text_embedding(sample_list)
  
      feature_sga, feature_cbn = self.process_feature_embedding(
          "image",
          sample_list,
          text_embedding_total,
          text_embedding_vec[:, 0],
      )
  
      joint_embedding = self.combine_embeddings(
          ["image", "text"],
          [feature_sga, feature_cbn, text_embedding_vec[:, 1]],
      )
  
      model_output = {"scores": self.calculate_logits(joint_embedding)}
  
      return model_output
  
imports: import copy
  from typing import Any, Dict, List, Optional, Tuple
  import omegaconf
  import torch
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.modules.embeddings import PreExtractedEmbedding, TextEmbedding, TwoBranchEmbedding
  from mmf.modules.layers import BranchCombineLayer, ClassifierLayer
  from mmf.utils.build import build_image_encoder
  from mmf.utils.general import filter_grads
  from omegaconf import DictConfig
label_desc: Use image node_id 20 for calling the MoVieMcan obj's underlying member method code with example usage: obj.forward(sample_list) and returns: model_output