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 basicblock(self, input, ch_out, name=None):
      conv1 = self._conv_norm(
          input,
          ch_out=ch_out,
          filter_size=1,
          stride=1,
          padding=0,
          name=name + ".0",
      )
      conv2 = self._conv_norm(
          conv1,
          ch_out=ch_out * 2,
          filter_size=3,
          stride=1,
          padding=1,
          name=name + ".1",
      )
      out = fluid.layers.elementwise_add(x=input, y=conv2, act=None)
      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 4 for calling the DarkNet obj's underlying member method code with example usage: obj.basicblock(input, ch_out, name) and returns: out