code: def build_dataloader(config, frcn_rois_per_img):
      """
      Builds the dataloader for the Faster-RCNN network using our aeon loader.
      Besides, the base loader, we add several operations:
      1. Cast the image data into float32 format
      2. Subtract the BGRMean from the image. We used pre-defined means from training
         the VGG network.
      3. Repack the data for Faster-RCNN model. This model has several nested branches, so
         The buffers have to repacked into nested tuples to match the branch leafs. Additionally,
         buffers for training the RCNN portion of the model are also allocated and provisioned
         to the model.
  
      Arguments:
          config (dict): dataloader configuration
          be (backend): compute backend
          frcn_rois_per_img (int): Number of ROIs to use for training the RCNN portion of the
              model. This is used to create the target buffers for RCNN.
  
      Returns:
          dataloader object.
      """
      dl = AeonDataLoader(config)
      dl = TypeCast(
          dl, index=0, dtype=np.float32
      )  # cast image to float
      dl = BGRMeanSubtract(
          dl, index=0, pixel_mean=util.FRCN_PIXEL_MEANS
      )  # subtract means
      dl = ObjectLocalization(
          dl, frcn_rois_per_img=frcn_rois_per_img
      )  # repack faster-rcnn
      return dl
  
file_path: neon/examples/faster-rcnn/faster_rcnn.py
imports: from neon.initializers import Constant, Gaussian
  from neon.transforms import Rectlin, Identity, Softmax, PixelwiseSoftmax
  from neon.layers import Conv, Affine, BranchNode, Tree, Dropout, RoiPooling
  from neon.models import Model
  from neon.data.dataloader_transformers import BGRMeanSubtract, TypeCast
  from neon.data.aeon_shim import AeonDataLoader
  from proposal_layer import ProposalLayer
  from objectlocalization import ObjectLocalization
  import util
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: build_dataloader(config, frcn_rois_per_img) and returns: dl