code: def get_model(model_name, batch_size, qconfig, original=False):
      gluon_model = gluon.model_zoo.vision.get_model(
          model_name, pretrained=True
      )
      img_size = 299 if model_name == "inceptionv3" else 224
      data_shape = (batch_size, 3, img_size, img_size)
      mod, params = relay.frontend.from_mxnet(
          gluon_model, {"data": data_shape}
      )
  
      logging.debug("original")
      logging.debug(mod.astext(show_meta_data=False))
      if original:
          return mod, params
  
      with qconfig:
          logging.debug("current quantize config")
          logging.debug(qtz.current_qconfig())
          qfunc = qtz.quantize(mod, params)
          logging.debug("after quantize")
          logging.debug(qfunc.astext(show_meta_data=False))
      return qfunc, params
  
file_path: tvm/tests/python/nightly/quantization/test_quantization_accuracy.py
imports: from collections import namedtuple
  import tvm
  from tvm import relay
  from tvm.relay import quantize
  import mxnet
  from mxnet import gluon
  import logging
  import os
  import tvm.testing
label_desc: Use image node_id 2 for calling a global function with example usage: get_model(model_name, batch_size, qconfig, original) and returns: qfunc,  params, mod,  params