class_docstr: For integration with MMF's trainer, datasets and other features,
  models needs to inherit this class, call `super`, write a build function,
  write a forward function taking a ``SampleList`` as input and returning a
  dict as output and finally, register it using ``@registry.register_model``
  
  Args:
      config (DictConfig): ``model_config`` configuration from global config.
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code: def load_requirements(self, config, *args, **kwargs):
      requirements = config.get("zoo_requirements", [])
      if isinstance(requirements, str):
          requirements = [requirements]
      for item in requirements:
          download_pretrained_model(item, *args, **kwargs)
  
imports: import collections
  import logging
  import warnings
  from copy import deepcopy
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Union
  import pytorch_lightning
  from mmf.common.registry import registry
  from mmf.common.report import Report
  from mmf.common.sample import SampleList, to_device
  from mmf.modules.losses import LossConfig, Losses
  from mmf.utils.checkpoint import load_pretrained_model
  from mmf.utils.checkpoint_updater import MMFToPLCheckpointUpdater
  from mmf.utils.download import download_pretrained_model
  from mmf.utils.file_io import PathManager
  from mmf.utils.general import get_current_device
  from mmf.utils.logger import log_class_usage
  from omegaconf import DictConfig, MISSING, OmegaConf
label_desc: Use image node_id 25 for calling the BaseModel obj's underlying member method code with example usage: obj.load_requirements(config) without return types