code: def test_predict_dependency(
      model, dataloaders_with_covariates, data_with_covariates, kwargs
  ):
      train_dataset = dataloaders_with_covariates["train"].dataset
      data_with_covariates = data_with_covariates.copy()
      dataset = TimeSeriesDataSet.from_dataset(
          train_dataset,
          data_with_covariates[
              lambda x: x.agency == data_with_covariates.agency.iloc[0]
          ],
          predict=True,
      )
      model.predict_dependency(
          dataset, variable="discount", values=[0.1, 0.0], **kwargs
      )
      model.predict_dependency(
          dataset,
          variable="agency",
          values=data_with_covariates.agency.unique()[:2],
          **kwargs,
      )
  
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 11 for calling a global function with example usage: test_predict_dependency(model, dataloaders_with_covariates, data_with_covariates, kwargs) without return types