code: def test_fit_params():
      iris = load_iris()
      X = iris.data
      y = iris.target
      sample_weight = np.ones(X.shape[0])
      forest = RandomForestClassifier(
          n_estimators=100, random_state=123
      )
      efs1 = EFS(
          forest,
          min_features=3,
          max_features=3,
          scoring="accuracy",
          cv=4,
          print_progress=False,
      )
      efs1 = efs1.fit(X, y, sample_weight=sample_weight)
      expect = {
          0: {
              "feature_idx": (0, 1, 2),
              "feature_names": ("0", "1", "2"),
              "cv_scores": np.array([0.947, 0.868, 0.919, 0.973]),
              "avg_score": 0.9269203413940257,
          },
          1: {
              "feature_idx": (0, 1, 3),
              "feature_names": ("0", "1", "3"),
              "cv_scores": np.array([0.921, 0.921, 0.892, 1.0]),
              "avg_score": 0.9337606837606838,
          },
          2: {
              "feature_idx": (0, 2, 3),
              "feature_names": ("0", "2", "3"),
              "cv_scores": np.array([0.974, 0.947, 0.919, 0.973]),
              "avg_score": 0.9532361308677098,
          },
          3: {
              "feature_idx": (1, 2, 3),
              "feature_names": ("1", "2", "3"),
              "cv_scores": np.array([0.974, 0.947, 0.892, 1.0]),
              "avg_score": 0.9532361308677098,
          },
      }
  
      if Version(sklearn_version) < Version("0.22"):
          expect[0]["avg_score"] = 0.9401709401709402
          expect[0]["cv_scores"] = np.array(
              [0.94871795, 0.92307692, 0.91666667, 0.97222222]
          )
          expect[1]["cv_scores"] = np.array(
              [0.94871795, 0.92307692, 0.91666667, 0.97222222]
          )
          expect[2]["cv_scores"] = np.array(
              [0.94871795, 0.92307692, 0.91666667, 0.97222222]
          )
          expect[2]["avg_score"] = 0.9599358974358974
          expect[3]["avg_score"] = 0.9599358974358974
          expect[3]["cv_scores"] = np.array(
              [0.97435897, 0.94871795, 0.91666667, 1.0]
          )
          assert round(efs1.best_score_, 4) == 0.9599
  
      else:
          assert round(efs1.best_score_, 4) == 0.9532
  
      dict_compare_utility(d1=expect, d2=efs1.subsets_)
      assert efs1.best_idx_ == (0, 2, 3)
  
imports: import numpy
  import pandas
  from numpy.testing import assert_almost_equal
  from packaging.version import Version
  from sklearn import __version__
  from sklearn.datasets import load_iris
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import GroupKFold
  from sklearn.neighbors import KNeighborsClassifier
  from mlxtend.classifier import SoftmaxRegression
  from mlxtend.data import boston_housing_data
  from mlxtend.feature_selection import ExhaustiveFeatureSelector
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 8 for calling a global function with example usage: test_fit_params() without return types