code: def test_yolo3_reset_class():
      test_classes = ["bird", "bicycle", "bus", "car", "cat"]
      test_classes_dict = dict(zip(test_classes, test_classes))
  
      ctx = mx.context.current_context()
      x = mx.random.uniform(
          shape=(1, 3, 512, 544), ctx=ctx
      )  # allow non-square and larger inputs
      model_name = "yolo3_darknet53_voc"
      net = gcv.model_zoo.get_model(
          model_name, pretrained=True, ctx=ctx
      )
      net.hybridize()
      net.reset_class(
          ["bus", "car", "bird"], reuse_weights=["bus", "car", "bird"]
      )
      net(x)
      mx.nd.waitall()
  
      net = gcv.model_zoo.get_model(
          model_name, pretrained=True, ctx=ctx
      )
      net.hybridize()
      net.reset_class(test_classes, reuse_weights=test_classes_dict)
      net(x)
      mx.nd.waitall()
  
      # for GPU
      ctx = mx.gpu(0)
      try:
          x = mx.random.uniform(shape=(1, 3, 512, 544), ctx=ctx)
      except Exception:
          return
      net = gcv.model_zoo.get_model(
          model_name, pretrained=True, ctx=ctx
      )
      net.hybridize()
      net.reset_class(["bus", "car", "bird"])
      net(x)
      mx.nd.waitall()
  
      net = gcv.model_zoo.get_model(
          model_name, pretrained=True, ctx=ctx
      )
      net.hybridize()
      net.reset_class(test_classes, reuse_weights=test_classes_dict)
      net(x)
      mx.nd.waitall()
  
imports: from __future__ import print_function
  import warnings
  import logging
  import mxnet
  from mxnet.contrib.quantization import *
  from ..unittests.common import try_gpu, with_cpu
  import gluoncv
label_desc: Use image node_id 14 for calling a global function with example usage: test_yolo3_reset_class() without return types