code: def create_classification_model_from_modelzoo(
      checkpoint_path: str,
      model: nn.Module,
  ) -> nn.Module:
      """
      Builds a model from PyTorchVideo's model zoo checkpoint.
  
      Example config for building this method can be found at -
      `pytorchvideo_trainer/conf/module/model/from_model_zoo_checkpoint.yaml`
  
      Args:
          checkpoint_path (str): Path the pretrained model weights.
          model (nn.Module): Module to load the checkpoints into.
      Returns:
          model (nn.Module): Returns the model with pretrained weights loaded.
      """
  
      with g_pathmgr.open(checkpoint_path, "rb") as f:
          checkpoint = torch.load(f, map_location="cpu")
      state_dict = checkpoint["model_state"]
      model.load_state_dict(state_dict)
      return model
  
imports: from dataclasses import dataclass
  from typing import Any, Dict, Iterable, List, Literal, Mapping, Optional, Tuple, TypedDict, Union
  import pytorch_lightning
  import torch
  from hydra.core.config_store import ConfigStore
  from hydra.utils import instantiate
  from iopath.common.file_io import g_pathmgr
  from omegaconf import MISSING, OmegaConf
  from pytorch_lightning.utilities import rank_zero_info
  from pytorchvideo_trainer.datamodule.transforms import MixVideoBatchWrapper
  from pytorchvideo_trainer.module.lr_policy import get_epoch_lr, LRSchedulerConf, set_lr
  from pytorchvideo_trainer.module.optimizer import construct_optimizer
  from torch import nn
  from torch.optim.lr_scheduler import _LRScheduler
  from torchrecipes.core.conf import ModuleConf
  from torchrecipes.utils.config_utils import get_class_name_str
label_desc: Use image node_id 2 for calling a global function with example usage: create_classification_model_from_modelzoo(checkpoint_path, model) and returns: model