code: def forward(self, sample_list):
      text_embedding_total = self.process_text_embedding(sample_list)
      text_embedding_total = text_embedding_total.new_zeros(
          text_embedding_total.size()
      )
  
      image_embedding_total, _ = self.process_feature_embedding(
          "image", sample_list, text_embedding_total
      )
  
      if self.inter_model is not None:
          image_embedding_total = self.inter_model(
              image_embedding_total
          )
  
      fa_image = (
          self.image_text_multi_modal_combine_layer.module.fa_image
      )
      dropout = self.image_text_multi_modal_combine_layer.module.dropout
  
      joint_embedding = dropout(fa_image(image_embedding_total))
  
      model_output = {"scores": self.calculate_logits(joint_embedding)}
  
      return model_output
  
imports: import copy
  import omegaconf
  import torch
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.modules.embeddings import ImageFeatureEmbedding, MultiHeadImageFeatureEmbedding, PreExtractedEmbedding, TextEmbedding
  from mmf.modules.layers import ClassifierLayer, ModalCombineLayer
  from mmf.utils.build import build_image_encoder
  from torch import nn
label_desc: Use image node_id 2 for calling the PythiaImageOnly obj's underlying member method code with example usage: obj.forward(sample_list) and returns: model_output