code: def predict(self, data_batch):
      self._mod.forward(data_batch)
      return dict(zip(self._mod.output_names, self._mod.get_outputs()))
  
imports: from __future__ import print_function
  import os
  import sys
  import time
  import mxnet
  import numpy
  from builtins import range
  from mxnet.module import Module
  from .module import MutableModule
  from rcnn.logger import logger
  from rcnn.config import config
  from rcnn.io import image
  from rcnn.processing.bbox_transform import bbox_pred, clip_boxes
  from rcnn.processing.nms import py_nms_wrapper, cpu_nms_wrapper, gpu_nms_wrapper
  from rcnn.processing.bbox_transform import bbox_overlaps
label_desc: Use image node_id 2 for calling the Predictor obj's underlying member method code with example usage: obj.predict(data_batch) and returns: dict