code: def __init__(self, stride=0, network="", dataset="", prefix=""):
      super(CascadeRefineOperator, self).__init__()
      self.stride = int(stride)
      self.prefix = prefix
      generate_config(network, dataset)
      self.mode = (
          config.TRAIN.OHEM_MODE
      )  # 0 for random 10:245, 1 for 10:246, 2 for 10:30, mode 1 for default
      stride = self.stride
      sstride = str(stride)
      base_size = config.RPN_ANCHOR_CFG[sstride]["BASE_SIZE"]
      allowed_border = config.RPN_ANCHOR_CFG[sstride]["ALLOWED_BORDER"]
      ratios = config.RPN_ANCHOR_CFG[sstride]["RATIOS"]
      scales = config.RPN_ANCHOR_CFG[sstride]["SCALES"]
      base_anchors = generate_anchors(
          base_size=base_size,
          ratios=list(ratios),
          scales=np.array(scales, dtype=np.float32),
          stride=stride,
          dense_anchor=config.DENSE_ANCHOR,
      )
      num_anchors = base_anchors.shape[0]
      feat_height, feat_width = (
          config.SCALES[0][0] // self.stride,
          config.SCALES[0][0] // self.stride,
      )
      feat_stride = self.stride
  
      A = num_anchors
      K = feat_height * feat_width
      self.A = A
  
      all_anchors = anchors_plane(
          feat_height, feat_width, feat_stride, base_anchors
      )
      all_anchors = all_anchors.reshape((K * A, 4))
      self.ori_anchors = all_anchors
      self.nbatch = 0
      global STAT
      for k in config.RPN_FEAT_STRIDE:
          STAT[k] = [0, 0, 0]
  
imports: from __future__ import print_function
  import sys
  import mxnet
  import numpy
  import datetime
  from distutils.util import strtobool
  from ..config import config, generate_config
  from ..processing.generate_anchor import generate_anchors, anchors_plane
  from ..processing.bbox_transform import bbox_overlaps, bbox_transform, landmark_transform
label_desc: Use image node_id 1 to create a new CascadeRefineOperator object from inherited base classes: mx.operator with example: obj = CascadeRefineOperator(stride, network, dataset, prefix)