code: def test_check_support_string_in_fixed_feature():
      iris = load_iris()
      X = iris.data
      y = iris.target
  
      features_names = [
          "sepal length",
          "sepal width",
          "petal length",
          "petal width",
      ]
      df = pd.DataFrame(X, columns=features_names)
  
      knn = KNeighborsClassifier(n_neighbors=4)
      efs1 = EFS(
          knn,
          min_features=2,
          max_features=3,
          scoring="accuracy",
          cv=0,
          print_progress=False,
          fixed_features=[features_names[0], features_names[1]],
      )
  
      efs1 = efs1.fit(df, y)
      # expect is based on what provided in `test_knn_wo_cv`
      expect = {
          0: {
              "feature_idx": (0, 1),
              "feature_names": (features_names[0], features_names[1]),
              "avg_score": 0.8333333333333334,
              "cv_scores": np.array([0.8333333333333334]),
          },
          1: {
              "feature_idx": (0, 1, 2),
              "feature_names": (
                  features_names[0],
                  features_names[1],
                  features_names[2],
              ),
              "avg_score": 0.95999999999999996,
              "cv_scores": np.array([0.96]),
          },
          2: {
              "feature_idx": (0, 1, 3),
              "feature_names": (
                  features_names[0],
                  features_names[1],
                  features_names[3],
              ),
              "avg_score": 0.96666666666666667,
              "cv_scores": np.array([0.96666667]),
          },
      }
      dict_compare_utility(d1=expect, d2=efs1.subsets_)
  
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 22 for calling a global function with example usage: test_check_support_string_in_fixed_feature() without return types