docstr: Test whether the categorical features are passed to LightGBMRegressor
code: def test_lgbm_categorical_features_passed_to_fit_correctly(
      self, lgb_fit_patch
  ):
      """Test whether the categorical features are passed to LightGBMRegressor"""
      (
          series,
          past_covariates,
          future_covariates,
      ) = self.inputs_for_tests_categorical_covariates
      self.lgbm_w_categorical_covariates.fit(
          series=series,
          past_covariates=past_covariates,
          future_covariates=future_covariates,
      )
  
      # Check that mocked super.fit() method was called with correct categorical_feature argument
      args, kwargs = lgb_fit_patch.call_args
      (
          cat_param_name,
          cat_param_default,
      ) = self.lgbm_w_categorical_covariates._categorical_fit_param
      assert kwargs[cat_param_name] == [2, 3, 5]
  
imports: import copy
  import functools
  import itertools
  import math
  from unittest.mock import patch
  import numpy
  import pandas
  import pytest
  from sklearn.ensemble import HistGradientBoostingRegressor, RandomForestRegressor
  from sklearn.linear_model import LinearRegression
  import darts
  from darts import TimeSeries
  from darts.dataprocessing.encoders import FutureCyclicEncoder, PastDatetimeAttributeEncoder
  from darts.logging import get_logger
  from darts.metrics import mae, rmse
  from darts.models import CatBoostModel, LightGBMModel, LinearRegressionModel, NotImportedModule, RandomForest, RegressionModel, XGBModel
  from darts.utils import timeseries_generation
  from darts.utils.multioutput import MultiOutputRegressor
label_desc: Use image node_id 34 for calling the TestRegressionModels obj's underlying member method code with example usage: obj.test_lgbm_categorical_features_passed_to_fit_correctly(lgb_fit_patch) without return types