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 _conv_norm(
      self,
      input,
      ch_out,
      filter_size,
      stride,
      padding,
      act="leaky",
      name=None,
  ):
      conv = fluid.layers.conv2d(
          input=input,
          num_filters=ch_out,
          filter_size=filter_size,
          stride=stride,
          padding=padding,
          act=None,
          param_attr=ParamAttr(name=name + ".conv.weights"),
          bias_attr=False,
      )
  
      bn_name = name + ".bn"
      bn_param_attr = ParamAttr(
          regularizer=L2Decay(float(self.norm_decay)),
          name=bn_name + ".scale",
      )
      bn_bias_attr = ParamAttr(
          regularizer=L2Decay(float(self.norm_decay)),
          name=bn_name + ".offset",
      )
  
      out = fluid.layers.batch_norm(
          input=conv,
          act=None,
          param_attr=bn_param_attr,
          bias_attr=bn_bias_attr,
          moving_mean_name=bn_name + ".mean",
          moving_variance_name=bn_name + ".var",
      )
  
      # leaky relu here has `alpha` as 0.1, can not be set by
      # `act` param in fluid.layers.batch_norm above.
      if act == "leaky":
          out = fluid.layers.leaky_relu(x=out, alpha=0.1)
  
      return out
  
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 2 for calling the DarkNet obj's underlying member method code with example usage: obj._conv_norm(input, ch_out, filter_size, stride, padding, act, name) and returns: out