code: def test_calib_models():
      model_list = [
          "resnet50_v1",
          "resnet50_v1d_0.11",
          "mobilenet1.0",
          "mobilenetv2_1.0",
          "squeezenet1.0",
          "squeezenet1.1",
          "vgg16",
      ]
      ctx = mx.context.current_context()
      x = mx.random.uniform(shape=(1, 3, 224, 224), ctx=ctx)
      _calib_model_list(model_list, ctx, x)
  
      model_list = ["inceptionv3"]
      x = mx.random.uniform(shape=(1, 3, 299, 299), ctx=ctx)
      _calib_model_list(model_list, ctx, x)
  
      model_list = [
          "ssd_300_vgg16_atrous_voc",
          "ssd_512_mobilenet1.0_voc",
          "ssd_512_resnet50_v1_voc",
          "ssd_512_vgg16_atrous_voc",
      ]
      ctx = mx.context.current_context()
      x = mx.random.uniform(shape=(1, 3, 512, 544), ctx=ctx)
      _calib_model_list(model_list, ctx, x)
  
      model_list = [
          "fcn_resnet101_voc",
          "fcn_resnet101_coco",
          "psp_resnet101_voc",
          "psp_resnet101_coco",
          "deeplab_resnet101_voc",
          "deeplab_resnet101_coco",
      ]
      ctx = mx.context.current_context()
      x = mx.random.uniform(shape=(1, 3, 480, 480), ctx=ctx)
      _calib_model_list(model_list, ctx, x)
  
      model_list = [
          "simple_pose_resnet18_v1b",
          "simple_pose_resnet50_v1b",
          "simple_pose_resnet50_v1d",
          "simple_pose_resnet101_v1b",
          "simple_pose_resnet101_v1d",
      ]
      ctx = mx.context.current_context()
      x = mx.random.uniform(shape=(1, 3, 256, 192), ctx=ctx)
      _calib_model_list(model_list, ctx, x)
  
      model_list = [
          "vgg16_ucf101",
          "resnet18_v1b_kinetics400",
          "resnet50_v1b_kinetics400",
      ]
      ctx = mx.context.current_context()
      x = mx.random.uniform(shape=(1, 3, 224, 224), ctx=ctx)
      _calib_model_list(model_list, ctx, x)
  
      model_list = ["inceptionv3_ucf101", "inceptionv3_kinetics400"]
      x = mx.random.uniform(shape=(1, 3, 299, 299), ctx=ctx)
      _calib_model_list(model_list, ctx, x)
  
      model_list = [
          "yolo3_darknet53_voc",
          "yolo3_darknet53_coco",
          "yolo3_mobilenet1.0_voc",
          "yolo3_mobilenet1.0_coco",
      ]
      x = mx.random.uniform(shape=(1, 3, 416, 416), ctx=ctx)
      _calib_model_list(model_list, ctx, x)
  
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 37 for calling a global function with example usage: test_calib_models() without return types