code: def test_integration(multiple_dataloaders_with_covariates, tmp_path):
      _integration(
          multiple_dataloaders_with_covariates,
          tmp_path,
          trainer_kwargs=dict(accelerator="cpu"),
      )
  
imports: import pickle
  import shutil
  import sys
  import lightning.pytorch
  from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
  from lightning.pytorch.loggers import TensorBoardLogger
  import numpy
  import pytest
  import torch
  from pytorch_forecasting import TimeSeriesDataSet
  from pytorch_forecasting.data import NaNLabelEncoder
  from pytorch_forecasting.data.encoders import GroupNormalizer, MultiNormalizer
  from pytorch_forecasting.metrics import CrossEntropy, MQF2DistributionLoss, MultiLoss, NegativeBinomialDistributionLoss, PoissonLoss, QuantileLoss, TweedieLoss
  from pytorch_forecasting.models import TemporalFusionTransformer
  from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters
  from test_models.conftest import make_dataloaders
label_desc: Use image node_id 1 for calling a global function with example usage: test_integration(multiple_dataloaders_with_covariates, tmp_path) without return types