code: def _build_output(self):
      # dynamic OCR-copying scores with pointer network
      self.ocr_ptr_net = OcrPtrNet(**self.config.classifier.ocr_ptr_net)
  
      # fixed answer vocabulary scores
      num_choices = registry.get(
          self._datasets[0] + "_num_final_outputs"
      )
      # remove the OCR copying dimensions in LoRRA's classifier output
      # (OCR copying will be handled separately)
      num_choices -= self.config.classifier.ocr_max_num
      self.classifier = ClassifierLayer(
          self.config.classifier.type,
          in_dim=self.mmt_config.hidden_size,
          out_dim=num_choices,
          **self.config.classifier.params,
      )
  
      self.answer_processor = registry.get(
          self._datasets[0] + "_answer_processor"
      )
  
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 9 for calling the M4C obj's underlying member method code with example usage: obj._build_output() without return types