code: def test_fit_cv(ray_start_4_cpus):
      train_dataset = ray.data.from_pandas(train_df)
      valid_dataset = ray.data.from_pandas(test_df)
      trainer = SklearnTrainer(
          estimator=RandomForestClassifier(),
          scaling_config=scale_config,
          label_column="target",
          cv=5,
          return_train_score_cv=True,
          datasets={
              TRAIN_DATASET_KEY: train_dataset,
              "valid": valid_dataset,
          },
      )
      result = trainer.fit()
  
      assert "valid" in result.metrics
      assert "cv" in result.metrics
      assert "test_score" in result.metrics["cv"]
      assert "test_score_mean" in result.metrics["cv"]
      assert "train_score" in result.metrics["cv"]
      assert "train_score_mean" in result.metrics["cv"]
  
file_path: ray/python/ray/train/tests/test_sklearn_trainer.py
imports: import pandas
  import pytest
  from sklearn.datasets import load_breast_cancer
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  import ray
  from ray import tune
  from ray.train import ScalingConfig
  from ray.train.constants import TRAIN_DATASET_KEY
  from ray.train.sklearn import SklearnTrainer
label_desc: Use image node_id 3 for calling a global function with example usage: test_fit_cv(ray_start_4_cpus) without return types