code: def test_init_shared_network(dataloaders_with_covariates):
      dataset = dataloaders_with_covariates["train"].dataset
      net = TemporalFusionTransformer.from_dataset(
          dataset, share_single_variable_networks=True
      )
      net.predict(dataset, fast_dev_run=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 8 for calling a global function with example usage: test_init_shared_network(dataloaders_with_covariates) without return types