code: def train_model(
      X_train_path: components.InputPath("np_array"),  # noqa: F821,N803
      y_train_path: components.InputPath("np_array"),  # noqa: F821
      model_path: components.OutputPath("sklearn_model"),  # noqa: F821
  ):
      import joblib
      import numpy as np
      from sklearn.ensemble import RandomForestClassifier
  
      with open(X_train_path, "rb") as f:
          X_train = np.load(f)  # noqa: N806
  
      with open(y_train_path, "rb") as f:
          y_train = np.load(f)
  
      model = RandomForestClassifier()
      model.fit(X_train, y_train)
  
      joblib.dump(model, model_path)
  
file_path: wandb/tests/functional_tests/t0_main/kfp/kfp-pipeline-sklearn.py
imports: import os
  import random
  from typing import NamedTuple
  import kfp
  import kfp.dsl
  from kfp import components
  from kubernetes.client.models import V1EnvVar
  from wandb.integration.kfp import wandb_log
  from wandb_probe import wandb_probe_package
label_desc: Use image node_id 3 for calling a global function with example usage: train_model(X_train_path, y_train_path, model_path) without return types