class_docstr: DepthwiseRPN
  get cls and loc throught z_f and x_f
  
  Parameters
  ----------
      bz : int
          batch size for train, bz = 1 if test.
      is_train : str
          is_train is True if train, False if test.
      ctx : mxnet.Context
          Context such as mx.cpu(), mx.gpu(0).
      anchor_num : int
          number of anchor.
      out_channels : int
          hidden feature channel.
class_docstr_tok: ["DepthwiseRPN","get","cls","and","loc","throught","z_f","and","x_f","Parameters","--","--","--","--","--","bz",":","int","batch","size","for","train",",","bz","=","1","if","test",".","is_train",":","str","is_train","is","True","if","train",",","False","if","test",".","ctx",":","mxnet.Context","Context","such","as","mx.cpu","(",")",",","mx.gpu","(","0",")",".","anchor_num",":","int","number","of","anchor",".","out_channels",":","int","hidden","feature","channel","."]
code: def __init__(
      self,
      bz=1,
      is_train=False,
      ctx=cpu(),
      anchor_num=5,
      out_channels=256,
  ):
      super(DepthwiseRPN, self).__init__()
      self.cls = DepthwiseXCorr(
          out_channels,
          2 * anchor_num,
          bz=bz,
          is_train=is_train,
          ctx=ctx,
      )
      self.loc = DepthwiseXCorr(
          out_channels,
          4 * anchor_num,
          bz=bz,
          is_train=is_train,
          ctx=ctx,
      )
  
file_path: gluon-cv/gluoncv/model_zoo/siamrpn/siam_rpn.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.context import cpu
label_desc: Use image node_id 1 to create a new DepthwiseRPN object from inherited base classes: RPN with example: obj = DepthwiseRPN(bz, is_train, ctx, anchor_num, out_channels)