code: def objective(trial):
      X, y = make_classification(n_features=10)
      X_train, X_test, y_train, y_test = train_test_split(X, y)
  
      C = trial.suggest_float("C", 1e-7, 10.0, log=True)
      solver = trial.suggest_categorical("solver", ("lbfgs", "saga"))
  
      clf = LogisticRegression(C=C, solver=solver)
      clf.fit(X_train, y_train)
      val_accuracy = clf.score(X_test, y_test)
  
      return val_accuracy
  
imports: import numpy
  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: val_accuracy