code: def process_image_detailed(self, img_path):
      _, cntk_img_input, dims = load_resize_and_pad(
          img_path, self._img_shape[2], self._img_shape[1]
      )
  
      cntk_dims_input = np.array(dims, dtype=np.float32)
      cntk_dims_input.shape = (1,) + cntk_dims_input.shape
      output = self._eval_model.eval(
          {
              self._eval_model.arguments[0]: [cntk_img_input],
              self._eval_model.arguments[1]: cntk_dims_input,
          }
      )
  
      out_dict = dict([(k.name, k) for k in output])
      out_cls_pred = output[out_dict["cls_pred"]][0]
      out_rpn_rois = output[out_dict["rpn_rois"]][0]
      out_bbox_regr = output[out_dict["bbox_regr"]][0]
  
      return out_cls_pred, out_rpn_rois, out_bbox_regr, dims
  
file_path: CNTK/Examples/Image/Detection/FasterRCNN/FasterRCNN_eval.py
imports: import os
  import numpy
  import cntk
  from cntk import input_variable, Axis
  from utils.map_helpers import evaluate_detections
  from utils.plot_helpers import load_resize_and_pad
  from utils.rpn.bbox_transform import regress_rois
  from utils.od_mb_source import ObjectDetectionMinibatchSource
label_desc: Use image node_id 3 for calling the FasterRCNN_Evaluator obj's underlying member method code with example usage: obj.process_image_detailed(img_path) and returns: out_cls_pred,  out_rpn_rois,  out_bbox_regr,  dims