code: def _get_bbox_regression_labels(bbox_target_data, num_classes):
      """Bounding-box regression targets (bbox_target_data) are stored in a
      compact form N x (class, tx, ty, tw, th)
      This function expands those targets into the 4-of-4*K representation used
      by the network (i.e. only one class has non-zero targets).
      Returns:
          bbox_target (ndarray): N x 4K blob of regression targets
          bbox_inside_weights (ndarray): N x 4K blob of loss weights
      """
  
      clss = bbox_target_data[:, 0]
      bbox_targets = np.zeros(
          (clss.size, 4 * num_classes), dtype=np.float32
      )
      bbox_inside_weights = np.zeros(
          bbox_targets.shape, dtype=np.float32
      )
      inds = np.where(clss > 0)[0]
      for ind in inds:
          cls = clss[ind]
          start = 4 * cls
          end = start + 4
          bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
          bbox_inside_weights[ind, start:end] = 1
      return bbox_targets, bbox_inside_weights
  
file_path: neon/examples/faster-rcnn/tests/proposal_target_layer_ref.py
imports: from __future__ import division
  from __future__ import print_function
  from builtins import object
  import numpy
  import numpy.random
  from util import bbox_transform, calculate_bb_overlap
label_desc: Use image node_id 1 for calling a global function with example usage: _get_bbox_regression_labels(bbox_target_data, num_classes) and returns: bbox_targets,  bbox_inside_weights