code: def __init__(
      self,
      img_map_file,
      roi_map_file,
      num_classes,
      max_annotations_per_image,
      pad_width,
      pad_height,
      pad_value,
      randomize,
      use_flipping,
      proposal_provider,
      proposal_iou_threshold=0.5,
      provide_targets=False,
      normalize_means=None,
      normalize_stds=None,
      max_images=None,
  ):
      self.image_si = StreamInformation(
          "image",
          0,
          "dense",
          np.float32,
          (
              3,
              pad_height,
              pad_width,
          ),
      )
      self.roi_si = StreamInformation(
          "annotation",
          1,
          "dense",
          np.float32,
          (
              max_annotations_per_image,
              5,
          ),
      )
      self.dims_si = StreamInformation(
          "dims", 1, "dense", np.float32, (4,)
      )
  
      if proposal_provider is not None:
          num_proposals = proposal_provider.num_proposals()
          self.proposals_si = StreamInformation(
              "proposals", 1, "dense", np.float32, (num_proposals, 4)
          )
          self.label_targets_si = StreamInformation(
              "label_targets",
              1,
              "dense",
              np.float32,
              (num_proposals, num_classes),
          )
          self.bbox_targets_si = StreamInformation(
              "bbox_targets",
              1,
              "dense",
              np.float32,
              (num_proposals, num_classes * 4),
          )
          self.bbiw_si = StreamInformation(
              "bbiw",
              1,
              "dense",
              np.float32,
              (num_proposals, num_classes * 4),
          )
      else:
          self.proposals_si = None
  
      self.od_reader = ObjectDetectionReader(
          img_map_file,
          roi_map_file,
          num_classes,
          max_annotations_per_image,
          pad_width,
          pad_height,
          pad_value,
          randomize,
          use_flipping,
          proposal_provider,
          proposal_iou_threshold,
          provide_targets,
          normalize_means,
          normalize_stds,
          max_images,
      )
  
      super(ObjectDetectionMinibatchSource, self).__init__()
  
file_path: CNTK/Examples/Image/Detection/utils/od_mb_source.py
imports: from cntk.io import UserMinibatchSource, StreamInformation, MinibatchData
  from cntk.core import Value
  from utils.od_reader import ObjectDetectionReader
  import numpy
label_desc: Use image node_id 1 to create a new ObjectDetectionMinibatchSource object from inherited base classes: UserMinibatchSource with example: obj = ObjectDetectionMinibatchSource(img_map_file, roi_map_file, num_classes, max_annotations_per_image, pad_width, pad_height, pad_value, randomize, use_flipping, proposal_provider, proposal_iou_threshold, provide_targets, normalize_means, normalize_stds, max_images)