code: def update_registry_for_pretrained(
      cls, config, checkpoint, full_output
  ):
      from omegaconf import OmegaConf
  
      # Hack datasets using OmegaConf
      datasets = full_output["full_config"].datasets
      dataset = datasets.split(",")[0]
      config_mock = OmegaConf.create({"datasets": datasets})
      registry.register("config", config_mock)
      registry.register(
          f"{dataset}_num_final_outputs",
          # Need to add as it is subtracted
          checkpoint["classifier.module.weight"].size(0)
          + config.classifier.ocr_max_num,
      )
      # Fix this later, when processor pipeline is available
      answer_processor = OmegaConf.create({"BOS_IDX": 1})
      registry.register(f"{dataset}_answer_processor", 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 18 for calling the M4C obj's underlying member method code with example usage: obj.update_registry_for_pretrained(cls, config, checkpoint, full_output) without return types