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 __init__(
      self,
      depth=53,
      norm_type="sync_bn",
      norm_decay=0.0,
      weight_prefix_name="",
      get_prediction=False,
      class_dim=1000,
  ):
      assert depth in [53], "unsupported depth value"
      self.depth = depth
      self.norm_type = norm_type
      self.norm_decay = norm_decay
      self.depth_cfg = {53: ([1, 2, 8, 8, 4], self.basicblock)}
      self.prefix_name = weight_prefix_name
      self.class_dim = class_dim
      self.get_prediction = get_prediction
  
file_path: PaddleHub/modules/image/classification/darknet53_imagenet/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 1 to create a new DarkNet object from inherited base classes: object with example: obj = DarkNet(depth, norm_type, norm_decay, weight_prefix_name, get_prediction, class_dim)