code: def objective(trial):
      # Categorical parameter
      optimizer = trial.suggest_categorical(
          "optimizer", ["MomentumSGD", "Adam"]
      )
  
      # Integer parameter
      num_layers = trial.suggest_int("num_layers", 1, 3)
  
      # Integer parameter (log)
      num_channels = trial.suggest_int(
          "num_channels", 32, 512, log=True
      )
  
      # Integer parameter (discretized)
      num_units = trial.suggest_int("num_units", 10, 100, step=5)
  
      # Floating point parameter
      dropout_rate = trial.suggest_float("dropout_rate", 0.0, 1.0)
  
      # Floating point parameter (log)
      learning_rate = trial.suggest_float(
          "learning_rate", 1e-5, 1e-2, log=True
      )
  
      # Floating point parameter (discretized)
      drop_path_rate = trial.suggest_float(
          "drop_path_rate", 0.0, 1.0, step=0.1
      )
  
imports: import optuna
  import sklearn.ensemble
  import sklearn.svm
label_desc: Use image node_id 1 for calling a global function with example usage: objective(trial) without return types