class_docstr: build SiamRPNTracker.predicted tracking result.and according to
  tracking result, product window penalty and smooth bbox
  
  Parameters
  ----------
      PENALTY_K : float
          Scale penalty
      WINDOW_INFLUENCE :float
          Window influence
      LR : float
          Interpolation learning rate
      EXEMPLAR_SIZE : int
          EXEMPLAR_SIZE
      INSTANCE_SIZE : int
          INSTANCE_SIZE
      BASE_SIZE : int
          Base size
      CONTEXT_AMOUNT : float
          Context amount
      STRIDE : int
          Anchor stride
      RATIOS : tuple
          Anchor ratios
      SCALES : tuple
          Anchor scales
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docstr: init z_crop Parameters
  
  Parameters
  ----------
  img : np.ndarray
      BGR image
  bbox: list or np.ndarray
      bbox
  ctx : mxnet.Context
      Context such as mx.cpu(), mx.gpu(0).
docstr_tok: ["init","z_crop","Parameters","Parameters","--","--","--","--","--","img",":","np.ndarray","BGR","image","bbox",":","list","or","np.ndarray","bbox","ctx",":","mxnet.Context","Context","such","as","mx.cpu","(",")",",","mx.gpu","(","0",")","."]
code: def init(self, img, bbox, ctx):
      """
      init z_crop Parameters
  
      Parameters
      ----------
      img : np.ndarray
          BGR image
      bbox: list or np.ndarray
          bbox
      ctx : mxnet.Context
          Context such as mx.cpu(), mx.gpu(0).
      """
      # center postion(x,y)
      self.center_pos = np.array(
          [bbox[0] + (bbox[2] - 1) / 2, bbox[1] + (bbox[3] - 1) / 2]
      )
      # size is (w,h)
      self.size = np.array([bbox[2], bbox[3]])
  
      # calculate z crop size
      w_z = self.size[0] + self.CONTEXT_AMOUNT * np.sum(self.size)
      h_z = self.size[1] + self.CONTEXT_AMOUNT * np.sum(self.size)
      s_z = round(np.sqrt(w_z * h_z))
  
      # calculate channle average
      self.channel_average = np.mean(img, axis=(0, 1))
  
      # get crop
      z_crop = self.get_subwindow(
          img,
          self.center_pos,
          self.EXEMPLAR_SIZE,
          s_z,
          self.channel_average,
          ctx,
      )
      self.model.template(z_crop)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import math
  from collections import namedtuple
  import numpy
  import mxnet
  from mxnet import nd
  from gluoncv.utils.filesystem import try_import_cv2
label_desc: Use image node_id 7 for calling the SiamRPNTracker obj's underlying member method code with example usage: obj.init(img, bbox, ctx) without return types