code: def test_hyperparameter_optimization_integration(
      dataloaders_with_covariates, tmp_path, use_learning_rate_finder
  ):
      train_dataloader = dataloaders_with_covariates["train"]
      val_dataloader = dataloaders_with_covariates["val"]
      try:
          optimize_hyperparameters(
              train_dataloaders=train_dataloader,
              val_dataloaders=val_dataloader,
              model_path=tmp_path,
              max_epochs=1,
              n_trials=3,
              log_dir=tmp_path,
              trainer_kwargs=dict(
                  fast_dev_run=True,
                  limit_train_batches=3,
                  # overwrite default trainer kwargs
                  enable_progress_bar=False,
              ),
              use_learning_rate_finder=use_learning_rate_finder,
              learning_rate_range=[1e-6, 1e-2],
          )
      finally:
          shutil.rmtree(tmp_path, ignore_errors=True)
  
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 18 for calling a global function with example usage: test_hyperparameter_optimization_integration(dataloaders_with_covariates, tmp_path, use_learning_rate_finder) without return types