code: def KITTI(
      manifest_file,
      manifest_root,
      rois_per_img=256,
      height=375,
      width=1242,
      inference=False,
  ):
      """
      Returns the aeon dataloader configuration for KITTI dataset.
      """
  
      CLASSES = (
          "__background__",
          "Car",
          "Van",
          "Truck",
          "Pedestrian",
          "Person_sitting",
          "Cyclist",
          "Tram",
          "Misc",
          "DontCare",
      )
  
      # if inference, we do not shuffle or flip
      do_transforms = not inference
  
      image_config = {"type": "image", "height": height, "width": width}
  
      localization_config = {
          "type": "localization_rcnn",
          "rois_per_image": rois_per_img,
          "height": height,
          "width": width,
          "class_names": CLASSES,
          "scaling_factor": 1.0 / 16,
      }
  
      augmentation = {
          "type": "image",
          "fixed_aspect_ratio": True,
          "flip_enable": do_transforms,
          "crop_enable": False,
      }
  
      return {
          "manifest_filename": manifest_file,
          "manifest_root": manifest_root,
          "etl": [image_config, localization_config],
          "cache_directory": get_data_cache_or_nothing(
              subdir="kitti_cache"
          ),
          "shuffle_enable": do_transforms,
          "shuffle_manifest": do_transforms,
          "batch_size": 1,
          "block_size": 100,
          "augmentation": [augmentation],
      }
  
file_path: neon/examples/faster-rcnn/objectlocalization.py
imports: import numpy
  from neon.data.dataloader_transformers import DataLoaderTransformer
  from neon.util.persist import get_data_cache_or_nothing
label_desc: Use image node_id 2 for calling a global function with example usage: KITTI(manifest_file, manifest_root, rois_per_img, height, width, inference) and returns: dict