code: def run_inference(mod):
      model = relay.create_executor("vm", mod, dev, target).evaluate()
      val_data, batch_fn = get_val_data()
      for i, batch in enumerate(val_data):
          data, label = batch_fn(batch)
          prediction = model(data)
          if (
              i > 10
          ):  # only run inference on a few samples in this tutorial
              break
  
file_path: tvm/gallery/how_to/deploy_models/deploy_quantized.py
imports: import tvm
  from tvm import te
  from tvm import relay
  import mxnet
  from tvm.contrib.download import download_testdata
  from mxnet import gluon
  import logging
  import os
label_desc: Use image node_id 5 for calling a global function with example usage: run_inference(mod) without return types