code: def _integration(
      data_with_covariates,
      tmp_path,
      cell_type="LSTM",
      data_loader_kwargs={},
      clip_target: bool = False,
      **kwargs,
  ):
      data_with_covariates = data_with_covariates.copy()
      if clip_target:
          data_with_covariates["target"] = data_with_covariates[
              "volume"
          ].clip(1e-3, 1.0)
      else:
          data_with_covariates["target"] = data_with_covariates[
              "volume"
          ]
      data_loader_default_kwargs = dict(
          target="target",
          time_varying_known_reals=["price_actual"],
          time_varying_unknown_reals=["target"],
          static_categoricals=["agency"],
          add_relative_time_idx=True,
      )
      data_loader_default_kwargs.update(data_loader_kwargs)
      dataloaders_with_covariates = make_dataloaders(
          data_with_covariates, **data_loader_default_kwargs
      )
      train_dataloader = dataloaders_with_covariates["train"]
      val_dataloader = dataloaders_with_covariates["val"]
      test_dataloader = dataloaders_with_covariates["test"]
  
      early_stop_callback = EarlyStopping(
          monitor="val_loss",
          min_delta=1e-4,
          patience=1,
          verbose=False,
          mode="min",
      )
  
      logger = TensorBoardLogger(tmp_path)
      trainer = pl.Trainer(
          max_epochs=3,
          gradient_clip_val=0.1,
          callbacks=[early_stop_callback],
          enable_checkpointing=True,
          default_root_dir=tmp_path,
          limit_train_batches=2,
          limit_val_batches=2,
          limit_test_batches=2,
          logger=logger,
      )
  
      net = RecurrentNetwork.from_dataset(
          train_dataloader.dataset,
          cell_type=cell_type,
          learning_rate=0.15,
          log_gradient_flow=True,
          log_interval=1000,
          hidden_size=5,
          **kwargs,
      )
      net.size()
      try:
          trainer.fit(
              net,
              train_dataloaders=train_dataloader,
              val_dataloaders=val_dataloader,
          )
          test_outputs = trainer.test(net, dataloaders=test_dataloader)
          assert len(test_outputs) > 0
          # check loading
          net = RecurrentNetwork.load_from_checkpoint(
              trainer.checkpoint_callback.best_model_path
          )
  
          # check prediction
          net.predict(
              val_dataloader,
              fast_dev_run=True,
              return_index=True,
              return_decoder_lengths=True,
          )
      finally:
          shutil.rmtree(tmp_path, ignore_errors=True)
  
      net.predict(
          val_dataloader,
          fast_dev_run=True,
          return_index=True,
          return_decoder_lengths=True,
      )
  
file_path: pytorch-forecasting/tests/test_models/test_rnn_model.py
imports: import pickle
  import shutil
  import lightning.pytorch
  from lightning.pytorch.callbacks import EarlyStopping
  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 MAE, CrossEntropy, QuantileLoss
  from pytorch_forecasting.models import RecurrentNetwork
label_desc: Use image node_id 1 for calling a global function with example usage: _integration(data_with_covariates, tmp_path, cell_type, data_loader_kwargs, clip_target) without return types