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 _upsample(self, input, scale=2, name=None):
      out = fluid.layers.resize_nearest(
          input=input, scale=float(scale), name=name
      )
      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 4 for calling the YOLOv3Head obj's underlying member method code with example usage: obj._upsample(input, scale, name) and returns: out