code: def step_two(data_frame, test_size=0.2, random_state=42):
      print("step_two")
      # make sure we have pandas for this step, we need it to use the data_frame
      import pandas as pd  # noqa
      from sklearn.model_selection import train_test_split
  
      y = data_frame["target"]
      X = data_frame[(c for c in data_frame.columns if c != "target")]
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=test_size, random_state=random_state
      )
  
      return X_train, X_test, y_train, y_test
  
imports: from clearml.automation.controller import PipelineDecorator
  from clearml import TaskTypes
label_desc: Use image node_id 2 for calling a global function with example usage: step_two(data_frame, test_size, random_state) and returns: X_train,  X_test,  y_train,  y_test