code: def test_integration(
      dataloaders_with_covariates,
      dataloaders_with_different_encoder_decoder_length,
      dataloaders_fixed_window_without_covariates,
      dataloaders_multi_target,
      tmp_path,
      dataloader,
  ):
      kwargs = {}
      if dataloader == "with_covariates":
          dataloader = dataloaders_with_covariates
          kwargs["backcast_loss_ratio"] = 0.5
      elif dataloader == "different_encoder_decoder_size":
          dataloader = dataloaders_with_different_encoder_decoder_length
      elif dataloader == "fixed_window_without_covariates":
          dataloader = dataloaders_fixed_window_without_covariates
      elif dataloader == "multi_target":
          dataloader = dataloaders_multi_target
          kwargs["loss"] = QuantileLoss()
      elif dataloader == "quantiles":
          dataloader = dataloaders_with_covariates
          kwargs["loss"] = QuantileLoss()
      elif dataloader == "implicit-quantiles":
          dataloader = dataloaders_with_covariates
          kwargs["loss"] = ImplicitQuantileNetworkDistributionLoss()
      elif dataloader == "multivariate-quantiles":
          dataloader = dataloaders_with_covariates
          kwargs["loss"] = MQF2DistributionLoss(
              prediction_length=dataloader[
                  "train"
              ].dataset.max_prediction_length
          )
          kwargs["learning_rate"] = 1e-9
          kwargs["trainer_kwargs"] = dict(accelerator="cpu")
      else:
          raise ValueError(f"dataloader {dataloader} unknown")
      _integration(dataloader, tmp_path=tmp_path, **kwargs)
  
file_path: pytorch-forecasting/tests/test_models/test_nhits.py
imports: import pickle
  import shutil
  import lightning.pytorch
  from lightning.pytorch.callbacks import EarlyStopping
  from lightning.pytorch.loggers import TensorBoardLogger
  import pytest
  from pytorch_forecasting.metrics import MQF2DistributionLoss, QuantileLoss
  from pytorch_forecasting.metrics.distributions import ImplicitQuantileNetworkDistributionLoss
  from pytorch_forecasting.models import NHiTS
label_desc: Use image node_id 2 for calling a global function with example usage: test_integration(dataloaders_with_covariates, dataloaders_with_different_encoder_decoder_length, dataloaders_fixed_window_without_covariates, dataloaders_multi_target, tmp_path, dataloader) without return types