code: def test_predict_average(model, dataloaders_with_covariates):
      prediction = model.predict(
          dataloaders_with_covariates["val"],
          fast_dev_run=True,
          mode="prediction",
          n_samples=100,
      )
      assert prediction.ndim == 2, "expected averaging of samples"
  
file_path: pytorch-forecasting/tests/test_models/test_deepar.py
imports: import pickle
  import shutil
  import lightning.pytorch
  from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
  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 BetaDistributionLoss, ImplicitQuantileNetworkDistributionLoss, LogNormalDistributionLoss, MultivariateNormalDistributionLoss, NegativeBinomialDistributionLoss, NormalDistributionLoss
  from pytorch_forecasting.models import DeepAR
label_desc: Use image node_id 4 for calling a global function with example usage: test_predict_average(model, dataloaders_with_covariates) without return types