code: def _calib_model_list(model_list, ctx, x, pretrained=True, **kwargs):
      pretrained_models = gcv.model_zoo.pretrained_model_list()
      for model in model_list:
          if model in pretrained_models:
              net = gcv.model_zoo.get_model(
                  model, pretrained=True, **kwargs
              )
          else:
              net = gcv.model_zoo.get_model(model, **kwargs)
              with warnings.catch_warnings():
                  warnings.simplefilter("ignore")
                  net.initialize()
          net.collect_params().reset_ctx(ctx)
          exclude_layers_match = ["flatten"]
          if (
              model.find("ssd") != -1
              or model.find("psp") != -1
              or model.find("deeplab") != -1
          ):
              exclude_layers_match += ["concat"]
          random_label = mx.random.uniform(shape=(x.shape[0], 1))
          dataset = mx.gluon.data.dataset.ArrayDataset(x, random_label)
          calib_data = mx.gluon.data.DataLoader(dataset, batch_size=1)
          dummy_logger = logging.getLogger("dummy")
          dummy_logger.setLevel(logging.ERROR)
          net = quantize_net(
              net,
              quantized_dtype="auto",
              exclude_layers=None,
              exclude_layers_match=exclude_layers_match,
              calib_data=calib_data,
              calib_mode="naive",
              num_calib_examples=1,
              ctx=ctx,
              logger=dummy_logger,
          )
          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 3 for calling a global function with example usage: _calib_model_list(model_list, ctx, x, pretrained) without return types