code: def model(dataloaders_with_covariates):
      dataset = dataloaders_with_covariates["train"].dataset
      net = TemporalFusionTransformer.from_dataset(
          dataset,
          learning_rate=0.15,
          hidden_size=4,
          attention_head_size=1,
          dropout=0.2,
          hidden_continuous_size=2,
          loss=PoissonLoss(),
          output_size=1,
          log_interval=5,
          log_val_interval=1,
          log_gradient_flow=True,
      )
      return net
  
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 6 for calling a global function with example usage: model(dataloaders_with_covariates) and returns: net