code: def model(dataloaders_with_covariates):
      dataset = dataloaders_with_covariates["train"].dataset
      net = RecurrentNetwork.from_dataset(
          dataset,
          learning_rate=0.15,
          log_gradient_flow=True,
          log_interval=1000,
          hidden_size=5,
      )
      return net
  
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 3 for calling a global function with example usage: model(dataloaders_with_covariates) and returns: net