code: def _forward_mmt(self, sample_list, fwd_results):
      # first forward the text BERT layers
      text_bert_out = self.text_bert(
          txt_inds=fwd_results["txt_inds"],
          txt_mask=fwd_results["txt_mask"],
      )
      fwd_results["txt_emb"] = self.text_bert_out_linear(text_bert_out)
  
      mmt_results = self.mmt(
          txt_emb=fwd_results["txt_emb"],
          txt_mask=fwd_results["txt_mask"],
          obj_emb=fwd_results["obj_mmt_in"],
          obj_mask=fwd_results["obj_mask"],
          ocr_emb=fwd_results["ocr_mmt_in"],
          ocr_mask=fwd_results["ocr_mask"],
          fixed_ans_emb=self.classifier.module.weight,
          prev_inds=fwd_results["prev_inds"],
      )
      fwd_results.update(mmt_results)
  
imports: import functools
  import logging
  import math
  import torch
  import torch.nn.functional
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.modules.layers import ClassifierLayer
  from mmf.utils.build import build_image_encoder
  from omegaconf import OmegaConf
  from torch import nn
label_desc: Use image node_id 14 for calling the M4C obj's underlying member method code with example usage: obj._forward_mmt(sample_list, fwd_results) without return types