code: def load_data():
      X, y = load_diabetes(return_X_y=True)
  
      min_max_scaler = preprocessing.MinMaxScaler()
      X_min_max = min_max_scaler.fit_transform(X)
      X_normalized = preprocessing.scale(X_min_max, with_std=False)
  
      X_train, X_test, y_train, y_test = train_test_split(
          X_normalized, y, test_size=0.2, random_state=42
      )
  
      y_train = y_train.reshape(-1, 1)
      y_test = y_test.reshape(-1, 1)
      return np.concatenate((X_train, y_train), axis=1), np.concatenate(
          (X_test, y_test), axis=1
      )
  
file_path: mlflow/examples/paddle/train_low_level_api.py
imports: import numpy
  import paddle
  import paddle.nn.functional
  from paddle.nn import Linear
  from sklearn import preprocessing
  from sklearn.datasets import load_diabetes
  from sklearn.model_selection import train_test_split
  import mlflow.paddle
label_desc: Use image node_id 1 for calling a global function with example usage: load_data() and returns: np,  np