code: def prepare_data():
      iris = load_iris()
      data = iris.data
      labels = iris.target
      target_names = iris.target_names
  
      X_train, X_test, y_train, y_test = train_test_split(
          data,
          labels,
          test_size=0.2,
          random_state=42,
          shuffle=True,
          stratify=labels,
      )
  
      X_train = torch.FloatTensor(X_train).to(device)
      X_test = torch.FloatTensor(X_test).to(device)
      y_train = torch.LongTensor(y_train).to(device)
      y_test = torch.LongTensor(y_test).to(device)
  
      return X_train, X_test, y_train, y_test, target_names
  
file_path: BentoML/examples/mlflow/torchscript/IrisClassification/iris_classification.py
imports: import argparse
  import mlflow.pytorch
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from sklearn.datasets import load_iris
  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: prepare_data() and returns: X_train,  X_test,  y_train,  y_test,  target_names