code: def main():
      # Load the dataset
      data = datasets.load_iris()
      X = data.data
      y = data.target
  
      # Three -> two classes
      X = X[y != 2]
      y = y[y != 2]
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.33
      )
  
      # Fit and predict using LDA
      lda = LDA()
      lda.fit(X_train, y_train)
      y_pred = lda.predict(X_test)
  
      accuracy = accuracy_score(y_test, y_pred)
      print("Accuracy:", accuracy)
  
      # Save model with BentoML
      saved_model = bentoml.picklable_model.save_model(
          "iris_clf_lda",
          lda,
          signatures={"predict": {"batchable": True}},
      )
      print(f"Model saved: {saved_model}")
  
imports: from __future__ import print_function
  from lda import LDA
  from sklearn import datasets
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  import bentoml
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types