code: def test_fit_params():
      X = np.array([[1, 1, 1], [0, 0, 0], [-1, -1, -1]] * 100)
      y = np.array([0, 0, 1] * 100)
  
      clf = DistRandomizedSearchCV(
          XGBClassifier(), {"max_depth": [3, 5]}, cv=3, n_iter=2
      )
      X_test = np.array([[1, 1, 0], [-2, 0, 5], [1, 1, 1]] * 10)
      y_test = np.array([1, 1, 0] * 10)
      fit_params = {
          "eval_metric": "logloss",
          "eval_set": [(X_test, y_test)],
          "early_stopping_rounds": 10,
      }
      clf.fit(X, y, **fit_params)
      preds = clf.predict(X[:3])
      assert np.allclose(preds, np.array([0, 0, 1]))
  
file_path: sk-dist/skdist/distribute/tests/test_search.py
imports: import sys
  import pytest
  import numpy
  from scipy.stats.distributions import expon
  from sklearn.linear_model import LogisticRegression
  from sklearn.naive_bayes import GaussianNB
  from sklearn.ensemble import RandomForestClassifier
label_desc: Use image node_id 5 for calling a global function with example usage: test_fit_params() without return types