class_docstr: DarkNet, see https://pjreddie.com/darknet/yolo/
  
  Args:
      depth (int): network depth, currently only darknet 53 is supported
      norm_type (str): normalization type, 'bn' and 'sync_bn' are supported
      norm_decay (float): weight decay for normalization layer weights
      get_prediction (bool): whether to get prediction
      class_dim (int): number of class while classification
code: def layer_warp(self, block_func, input, ch_out, count, name=None):
      out = block_func(input, ch_out=ch_out, name="{}.0".format(name))
      for j in six.moves.xrange(1, count):
          out = block_func(
              out, ch_out=ch_out, name="{}.{}".format(name, j)
          )
      return out
  
file_path: PaddleHub/modules/image/object_detection/yolov3_darknet53_venus/darknet.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import six
  import math
  from paddle import fluid
  from paddle.fluid.param_attr import ParamAttr
  from paddle.fluid.regularizer import L2Decay
label_desc: Use image node_id 5 for calling the DarkNet obj's underlying member method code with example usage: obj.layer_warp(block_func, input, ch_out, count, name) and returns: out