code: def test_image_regression_estimator():
      from gluoncv.auto.estimators import ImageClassificationEstimator
  
      est = ImageClassificationEstimator(
          {
              "train": {"epochs": 3, "batch_size": 8},
              "gpus": list(range(get_gpu_count())),
          },
          problem_type="regression",
      )
      res = est.fit(IMAGE_REGRESS_DATASET)
      assert res.get("valid_score", 3) < 3
      test_result = est.predict(IMAGE_REGRESS_TEST)
      test_result = est.predict(IMAGE_REGRESS_TEST, with_proba=True)
      est.predict(IMAGE_REGRESS_TEST.iloc[0]["image"])
      evaluate_result = est.evaluate(
          IMAGE_REGRESS_TEST, metric_name="rmse"
      )
      feature = est.predict_feature(IMAGE_REGRESS_TEST)
      est.predict_feature(IMAGE_REGRESS_TEST.iloc[0]["image"])
      # test save/load
      _save_load_test(est, "img_regression.pkl")
  
file_path: gluon-cv/tests/auto/test_auto_estimators.py
imports: from PIL import Image
  import numpy
  from gluoncv.auto.tasks import ImageClassification, ImagePrediction
  from gluoncv.auto.tasks import ObjectDetection
  from autogluon.core.scheduler.resource import get_cpu_count, get_gpu_count
label_desc: Use image node_id 1 for calling a global function with example usage: test_image_regression_estimator() without return types