code: def _get_dataset(dataset, args):
      if dataset.lower() == "voc":
          train_dataset = gdata.VOCDetection(
              splits=[(2007, "trainval"), (2012, "trainval")]
          )
          val_dataset = gdata.VOCDetection(splits=[(2007, "test")])
          val_metric = VOC07MApMetric(
              iou_thresh=0.5, class_names=val_dataset.classes
          )
      elif dataset.lower() == "voc_tiny":
          # need to download the dataset and specify the path to store the dataset in
          # root = os.path.expanduser('~/.mxnet/datasets/')
          # filename_zip = ag.download('https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip', path=root)
          # filename = ag.unzip(filename_zip, root=root)
          # data_root = os.path.join(root, filename)
          train_dataset = gdata.CustomVOCDetectionBase(
              classes=("motorbike",),
              root=args.dataset_root + "tiny_motorbike",
              splits=[("", "trainval")],
          )
          val_dataset = gdata.CustomVOCDetectionBase(
              classes=("motorbike",),
              root=args.dataset_root + "tiny_motorbike",
              splits=[("", "test")],
          )
          val_metric = VOC07MApMetric(
              iou_thresh=0.5, class_names=val_dataset.classes
          )
      elif dataset.lower() in ["clipart", "comic", "watercolor"]:
          root = os.path.join(
              "~", ".mxnet", "datasets", dataset.lower()
          )
          train_dataset = gdata.CustomVOCDetection(
              root=root, splits=[("", "train")], generate_classes=True
          )
          val_dataset = gdata.CustomVOCDetection(
              root=root, splits=[("", "test")], generate_classes=True
          )
          val_metric = VOC07MApMetric(
              iou_thresh=0.5, class_names=val_dataset.classes
          )
      elif dataset.lower() == "coco":
          train_dataset = gdata.COCODetection(
              splits="instances_train2017", use_crowd=False
          )
          val_dataset = gdata.COCODetection(
              splits="instances_val2017", skip_empty=False
          )
          val_metric = COCODetectionMetric(
              val_dataset,
              os.path.join(args.logdir, args.save_prefix + "_eval"),
              cleanup=True,
          )
      else:
          raise NotImplementedError(
              "Dataset: {} not implemented.".format(dataset)
          )
      if args.train.mixup:
          from gluoncv.data.mixup import detection
  
          train_dataset = detection.MixupDetection(train_dataset)
      return train_dataset, val_dataset, val_metric
  
file_path: gluon-cv/gluoncv/auto/estimators/faster_rcnn/utils.py
imports: import os
  from mxnet import gluon
  from ....data.batchify import FasterRCNNTrainBatchify, Tuple, Append
  from ....data.sampler import SplitSortedBucketSampler
  from ....data.transforms.presets.rcnn import FasterRCNNDefaultValTransform
  from ....None import data
  from ....utils.metrics.coco_detection import COCODetectionMetric
  from ....utils.metrics.voc_detection import VOC07MApMetric
label_desc: Use image node_id 6 for calling a global function with example usage: _get_dataset(dataset, args) and returns: train_dataset,  val_dataset,  val_metric