code: def model(dataloaders_with_covariates):
      dataset = dataloaders_with_covariates["train"].dataset
      net = NHiTS.from_dataset(
          dataset,
          learning_rate=0.15,
          hidden_size=8,
          log_gradient_flow=True,
          log_interval=1000,
          backcast_loss_ratio=1.0,
      )
      return net
  
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 3 for calling a global function with example usage: model(dataloaders_with_covariates) and returns: net