code: def test_tvmc_workflow(use_vm, keras_simple):
      pytest.importorskip("tensorflow")
      import tensorflow as tf
  
      # Reset so the input name remains consistent across unit test runs
      tf.keras.backend.clear_session()
  
      tvmc_model = tvmc.load(keras_simple)
      tuning_records = tvmc.tune(
          tvmc_model, target="llvm", enable_autoscheduler=True, trials=2
      )
      tvmc_package = tvmc.compile(
          tvmc_model,
          tuning_records=tuning_records,
          target="llvm",
          use_vm=use_vm,
      )
      input_dict = {
          "input_1": np.random.uniform(size=(1, 32, 32, 3)).astype(
              "float32"
          )
      }
  
      result = tvmc.run(
          tvmc_package,
          device="cpu",
          end_to_end=True,
          benchmark=True,
          inputs=input_dict,
      )
      assert type(tvmc_model) is TVMCModel
      assert type(tvmc_package) is TVMCPackage
      assert type(result) is TVMCResult
      assert path.exists(tuning_records)
      assert type(result.outputs) is dict
      assert type(result.times) is BenchmarkResult
      assert "output_0" in result.outputs.keys()
  
file_path: tvm/tests/python/driver/tvmc/test_model.py
imports: import platform
  import pytest
  import os
  import numpy
  from os import path
  from tvm.driver import tvmc
  from tvm.driver.tvmc.model import TVMCModel, TVMCPackage, TVMCResult
  from tvm.runtime.module import BenchmarkResult
label_desc: Use image node_id 1 for calling a global function with example usage: test_tvmc_workflow(use_vm, keras_simple) without return types