class_docstr: Head block for YOLOv3 network
  
  Args:
      norm_decay (float): weight decay for normalization layer weights
      num_classes (int): number of output classes
      ignore_thresh (float): threshold to ignore confidence loss
      label_smooth (bool): whether to use label smoothing
      anchors (list): anchors
      anchor_masks (list): anchor masks
      nms (object): an instance of `MultiClassNMS`
class_docstr_tok: ["Head","block","for","YOLOv3","network","Args",":","norm_decay","(","float",")",":","weight","decay","for","normalization","layer","weights","num_classes","(","int",")",":","number","of","output","classes","ignore_thresh","(","float",")",":","threshold","to","ignore","confidence","loss","label_smooth","(","bool",")",":","whether","to","use","label","smoothing","anchors","(","list",")",":","anchors","anchor_masks","(","list",")",":","anchor","masks","nms","(","object",")",":","an","instance","of","`","MultiClassNMS","`"]
code: def _conv_bn(
      self,
      input,
      ch_out,
      filter_size,
      stride,
      padding,
      act="leaky",
      is_test=True,
      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(self.norm_decay), name=bn_name + ".scale"
      )
      bn_bias_attr = ParamAttr(
          regularizer=L2Decay(self.norm_decay), name=bn_name + ".offset"
      )
      out = fluid.layers.batch_norm(
          input=conv,
          act=None,
          is_test=is_test,
          param_attr=bn_param_attr,
          bias_attr=bn_bias_attr,
          moving_mean_name=bn_name + ".mean",
          moving_variance_name=bn_name + ".var",
      )
  
      if act == "leaky":
          out = fluid.layers.leaky_relu(x=out, alpha=0.1)
      return out
  
file_path: PaddleHub/modules/image/object_detection/yolov3_darknet53_venus/yolo_head.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from collections import OrderedDict
  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 YOLOv3Head obj's underlying member method code with example usage: obj._conv_bn(input, ch_out, filter_size, stride, padding, act, is_test, name) and returns: out