code: def forward(self, sample_list):
      v = sample_list.image_feature_0
      q = self.word_embedding(sample_list.text)
  
      q_emb = self.q_emb.forward_all(q)
  
      b_emb = [0] * self.config.bilinear_attention.gamma
      att, logits = self.v_att.forward_all(v, q_emb)
  
      for g in range(self.config.bilinear_attention.gamma):
          g_att = att[:, g, :, :]
          b_emb[g] = self.b_net[g].forward_with_weights(v, q_emb, g_att)
          q_emb = self.q_prj[g](b_emb[g].unsqueeze(1)) + q_emb
  
      logits = self.classifier(q_emb.sum(1))
  
      return {"scores": logits}
  
file_path: mmf/mmf/models/ban.py
imports: import torch
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.modules.embeddings import BiLSTMTextEmbedding
  from mmf.modules.layers import BCNet, BiAttention, FCNet, WeightNormClassifier
  from torch import nn
label_desc: Use image node_id 8 for calling the BAN obj's underlying member method code with example usage: obj.forward(sample_list) and returns: dict