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
      )
      sfs = SFS(estimator=forest, verbose=0)
      sfs.fit(X, y, sample_weight=sample_weight)
      assert sfs.k_feature_idx_ == (3,)
  
imports: import numpy
  from numpy.testing import assert_almost_equal
  from sklearn.datasets import load_iris
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import roc_auc_score
  from sklearn.neighbors import KNeighborsClassifier
  from mlxtend.data import boston_housing_data
  from mlxtend.feature_selection import SequentialFeatureSelector
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 3 for calling a global function with example usage: test_fit_params() without return types