class_docstr: Transforms the dataloader inputs for provisioning to the Faster-RCNN model.
  Provided dataloader must be of the (image, localization_rcnn) type.
  
  To support the Faster-RCNN model, this transformer:
  1. allocates buffers to start the targets for the classification layers. These
     buffers are populated during fprop by part of the network.
  2. stores metadata for each image that are populated with each minibatch.
  3. provides functions for the Faster-RCNN network to access the above target
     and metdata buffers.
  4. Repacks the buffers provided by the dataloader for the tree structure of the
     Faster-RCNN model.
  
  Args:
      dataloader (DataLoader): dataloader to transform
      frcn_rois_per_img (int): how many rois to sample to train frcnn
code: def get_metadata_buffers(self):
      return (
          self.im_shape,
          self.im_scale,
          self.gt_boxes,
          self.gt_classes,
          self.num_gt_boxes,
          self.difficult,
      )
  
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 4 for calling the ObjectLocalization obj's underlying member method code with example usage: obj.get_metadata_buffers() and returns: self,  self,  self,  self,  self,  self