code: def auto_suggest(config, estimator, logger):
      """
      Automatically suggest some hyperparameters based on the dataset statistics.
      """
      # specify estimator search space
      if estimator is None:
          estimator_init = [
              SSDEstimator,
              YOLOv3Estimator,
              FasterRCNNEstimator,
              CenterNetEstimator,
          ]
          config["estimator"] = ag.Categorical(*estimator_init)
      elif isinstance(estimator, str):
          named_estimators = {
              "ssd": SSDEstimator,
              "faster_rcnn": FasterRCNNEstimator,
              "yolo3": YOLOv3Estimator,
              "center_net": CenterNetEstimator,
              "img_cls": ImageClassificationEstimator,
          }
          if estimator.lower() in named_estimators:
              estimator = [named_estimators[estimator.lower()]]
          else:
              available_ests = named_estimators.keys()
              raise ValueError(
                  f"Unknown estimator name: {estimator}, options: {available_ests}"
              )
      elif isinstance(estimator, (tuple, list)):
          pass
      else:
          if isinstance(estimator, ag.Space):
              estimator = estimator.data
          elif isinstance(estimator, str):
              estimator = [estimator]
          for i, e in enumerate(estimator):
              if e == "ssd":
                  estimator[i] = SSDEstimator
              elif e == "yolo3":
                  estimator[i] = YOLOv3Estimator
              elif e == "faster_rcnn":
                  estimator[i] = FasterRCNNEstimator
              elif e == "center_net":
                  estimator[i] = CenterNetEstimator
          if not estimator:
              raise ValueError(
                  "Unable to determine the estimator for fit function."
              )
          if len(estimator) == 1:
              config["estimator"] = estimator[0]
          else:
              config["estimator"] = ag.Categorical(*estimator)
  
      # get dataset statistics
      # user needs to define a Dataset object "train_dataset" when using custom dataset
      train_dataset = config.get("train_dataset", None)
      try:
          if train_dataset is None:
              dataset_name = config.get("dataset", "voc")
              dataset_root = config.get(
                  "dataset_root", "~/.mxnet/datasets/"
              )
              if gdata is not None and dataset_name == "voc":
                  train_dataset = gdata.VOCDetection(
                      splits=[(2007, "trainval"), (2012, "trainval")]
                  )
              elif gdata is not None and dataset_name == "voc_tiny":
                  train_dataset = gdata.CustomVOCDetectionBase(
                      classes=("motorbike",),
                      root=dataset_root + "tiny_motorbike",
                      splits=[("", "trainval")],
                  )
              elif gdata is not None and dataset_name == "coco":
                  train_dataset = gdata.COCODetection(
                      splits=["instances_train2017"]
                  )
              else:
                  if gdata is None:
                      logger.warning(
                          "Unable to import mxnet so voc and coco formats are currently not loaded."
                      )
          elif isinstance(train_dataset, ObjectDetectionDataset):
              train_dataset = train_dataset.to_mxnet()
          else:
              logger.info("Unknown dataset, quit auto suggestion...")
              return
      # pylint: disable=broad-except
      except Exception as e:
          logger.info(f"Unexpected error: {e}, quit auto suggestion...")
          return
  
      # choose 100 examples to calculate average statistics
      num_examples = 100
      image_size_list = []
      num_objects_list = []
      bbox_size_list = []
      bbox_rel_size_list = []
  
      for i in range(num_examples):
          train_image, train_label = train_dataset[i]
  
          image_height = train_image.shape[0]
          image_width = train_image.shape[1]
          image_size = image_height * image_width
          image_size_list.append(image_size)
  
          bounding_boxes = train_label[:, :4]
          num_objects = bounding_boxes.shape[0]
          bbox_height = bounding_boxes[:, 3] - bounding_boxes[:, 1]
          bbox_width = bounding_boxes[:, 2] - bounding_boxes[:, 0]
          bbox_size = bbox_height * bbox_width
          bbox_rel_size = bbox_size / image_size
          num_objects_list.append(num_objects)
          bbox_size_list.append(np.mean(bbox_size))
          bbox_rel_size_list.append(np.mean(bbox_rel_size))
  
      num_images = len(train_dataset)
      image_size = np.mean(image_size_list)
      try:
          num_classes = len(train_dataset.CLASSES)
      except AttributeError:
          num_classes = len(train_dataset.classes)
      num_objects = np.mean(num_objects_list)
      bbox_size = np.mean(bbox_size_list)
      bbox_rel_size = np.mean(bbox_rel_size_list)
  
      logger.info("[Printing dataset statistics]...")
      logger.info("number of training images: %d", num_images)
      logger.info("average image size: %.2f", image_size)
      logger.info("number of total object classes: %d", num_classes)
      logger.info(
          "average number of objects in an image: %.2f", num_objects
      )
      logger.info("average bounding box size: %.2f", bbox_size)
      logger.info(
          "average bounding box relative size: %.2f", bbox_rel_size
      )
  
      # specify 3 parts of config: data preprocessing, model selection, training settings
      if bbox_rel_size < 0.2 or num_objects > 5:
          suggested_estimator = [FasterRCNNEstimator]
      else:
          suggested_estimator = [
              SSDEstimator,
              YOLOv3Estimator,
              CenterNetEstimator,
          ]
  
      # specify estimator search space based on suggestion
      if estimator is None:
          estimator = suggested_estimator
          config["estimator"] = ag.Categorical(*estimator)
  
imports: import copy
  import warnings
  import numpy
  import autogluon.core
  from ..estimators.base_estimator import BaseEstimator
  from ..estimators import SSDEstimator, FasterRCNNEstimator, YOLOv3Estimator, CenterNetEstimator
  from ..estimators import ImageClassificationEstimator
  from ..data.dataset import ObjectDetectionDataset
label_desc: Use image node_id 1 for calling a global function with example usage: auto_suggest(config, estimator, logger) without return types