code: def test_pickle(dataloaders_with_covariates, loss):
      dataset = dataloaders_with_covariates["train"].dataset
      model = DeepAR.from_dataset(
          dataset,
          hidden_size=5,
          learning_rate=0.15,
          log_gradient_flow=True,
          log_interval=1000,
          loss=loss,
      )
      pkl = pickle.dumps(model)
      pickle.loads(pkl)
  
file_path: pytorch-forecasting/tests/test_models/test_deepar.py
imports: import pickle
  import shutil
  import lightning.pytorch
  from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
  from lightning.pytorch.loggers import TensorBoardLogger
  import pytest
  from test_models.conftest import make_dataloaders
  from torch import nn
  from pytorch_forecasting.data.encoders import GroupNormalizer
  from pytorch_forecasting.metrics import BetaDistributionLoss, ImplicitQuantileNetworkDistributionLoss, LogNormalDistributionLoss, MultivariateNormalDistributionLoss, NegativeBinomialDistributionLoss, NormalDistributionLoss
  from pytorch_forecasting.models import DeepAR
label_desc: Use image node_id 6 for calling a global function with example usage: test_pickle(dataloaders_with_covariates, loss) without return types