question: In load_from_checkpoint, "TypeError: __init__ () missing 1 required positional argument:'cfg'"
context: I am experimenting with the following repository. Keiku/PyTorch-Lightning-CIFAR10: "Not too complicated" training code for CIFAR-10 by PyTorch Lightning
  It is implemented as follows. I was able to train/validation. But test has not been implemented yet.
  import os
  
  import hydra
  import torch
  from hydra.core.hydra_config import HydraConfig
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer, seed_everything
  from pytorch_lightning.callbacks import ModelCheckpoint
  from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger
  
  from datamodule import LitCIFAR10DataModule
  from model import LitCIFAR10Model
  
  
  @hydra.main(config_path="./configs", config_name="default.yaml")
  def main(cfg: DictConfig) -> None:
  
      if "experiments" in cfg.keys():
          cfg = OmegaConf.merge(cfg, cfg.experiments)
  
      seed_everything(0)
      os.environ["CUDA_VISIBLE_DEVICES"] = cfg.runs.gpu_id
  
      if cfg.runs.logger == "wandb":
          logger = WandbLogger(name=cfg.model.classifier, project="cifar10")
      elif cfg.runs.logger == "tensorboard":
          logger = TensorBoardLogger(cfg.train.tensorboard_dir, name=cfg.model.classifier)
  
      checkpoint = ModelCheckpoint(monitor="acc/val", mode="max", save_last=True)
  
      trainer = Trainer(
          fast_dev_run=cfg.runs.dev,
          logger=logger if not (cfg.runs.dev or cfg.runs.evaluate) else None,
          gpus=-1,
          deterministic=True,
          weights_summary=None,
          log_every_n_steps=1,
          max_epochs=cfg.train.num_epochs,
          checkpoint_callback=checkpoint,
          precision=cfg.runs.precision,
          resume_from_checkpoint=cfg.train.checkpoint,
      )
  
      datamodule = LitCIFAR10DataModule(cfg)
      model = LitCIFAR10Model(cfg)
  
      if cfg.runs.evaluate:
          model = LitCIFAR10Model.load_from_checkpoint(checkpoint_path=cfg.test.checkpoint)
          trainer.test(model, datamodule.test_dataloader())
      else:
          trainer.fit(model, datamodule)
          trainer.test()
  
  if __name__ == "__main__":
      main()
  I tried using load_from_checkpoint for test, but I get the following error. How can I solve it?
  ⋊> /m/n/k/c/PyTorch-Lightning-CIFAR10 on main ⨯
  pipenv run python train.py +experiments=test_exp01 hydra.run.dir=outputs/test_exp01
  Global seed set to 0
  GPU available: True, used: True
  TPU available: None, using: 0 TPU cores
  LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
  Traceback (most recent call last):
    File "train.py", line 48, in main
      model = LitCIFAR10Model.load_from_checkpoint(checkpoint_path=cfg.test.checkpoint)
    File "/home/anasys/.local/share/virtualenvs/PyTorch-Lightning-CIFAR10-fAnnMMRx/lib/python3.8/site-packages/pytorch_lightning/core/saving.py", line 159, in load_from_checkpoint
      model = cls._load_model_state(checkpoint, strict=strict, **kwargs)
    File "/home/anasys/.local/share/virtualenvs/PyTorch-Lightning-CIFAR10-fAnnMMRx/lib/python3.8/site-packages/pytorch_lightning/core/saving.py", line 199, in _load_model_state
      model = cls(**_cls_kwargs)
  TypeError: __init__() missing 1 required positional argument: 'cfg'
  
  Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
  ⋊> /m/n/k/c/PyTorch-Lightning-CIFAR10 on main ⨯
answer: I was able to infer test by setting as follows. I think the documentation is missing, so please refer to my implementation.
      if cfg.runs.evaluate:
          hparams = OmegaConf.load(cfg.test.hparams)
          model = LitCIFAR10Model.load_from_checkpoint(checkpoint_path=cfg.test.checkpoint, **hparams)
          trainer.test(model, datamodule.test_dataloader())
      else:
          trainer.fit(model, datamodule)
          trainer.test()