code: def test_when_static_and_dynamic_covariates_present_then_model_trains_normally(
      model_class,
  ):
      dataframe_with_static_and_covariates = (
          DATAFRAME_WITH_STATIC.copy()
      )
      known_covariates_names = ["cov1", "cov2"]
      for col_name in known_covariates_names:
          dataframe_with_static_and_covariates[
              col_name
          ] = np.random.normal(
              size=len(dataframe_with_static_and_covariates)
          )
  
      gen = TimeSeriesFeatureGenerator(
          target="target", known_covariates_names=known_covariates_names
      )
      df = gen.fit_transform(dataframe_with_static_and_covariates)
  
      model = model_class(
          hyperparameters=DUMMY_HYPERPARAMETERS,
          metadata=gen.covariate_metadata,
          freq=df.freq,
      )
      model.fit(train_data=df)
      model.score_and_cache_oof(df)
  
imports: from unittest import mock
  import numpy
  import pytest
  from gluonts.model.predictor import Predictor
  from autogluon.timeseries.models.gluonts import DeepARModel, DLinearModel, PatchTSTModel, SimpleFeedForwardModel, TemporalFusionTransformerModel, WaveNetModel
  from autogluon.timeseries.utils.features import TimeSeriesFeatureGenerator
  from ..common import DATAFRAME_WITH_COVARIATES, DATAFRAME_WITH_STATIC, DUMMY_TS_DATAFRAME
label_desc: Use image node_id 15 for calling a global function with example usage: test_when_static_and_dynamic_covariates_present_then_model_trains_normally(model_class) without return types