code: def objective(trial):
      X, y = make_classification(n_features=10, random_state=1)
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, random_state=1
      )
  
      C = trial.suggest_float("C", 1e-7, 10.0, log=True)
  
      clf = LogisticRegression(C=C)
      clf.fit(X_train, y_train)
  
      return clf.score(X_test, y_test)
  
imports: from sklearn import metrics
  from sklearn.datasets import make_classification
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import train_test_split
  import optuna
label_desc: Use image node_id 1 for calling a global function with example usage: objective(trial) and returns: clf