code: def evaluate_performance(lib, data_shape):
      # upload parameters to device
      dev = tvm.cpu()
      data_tvm = tvm.nd.array(
          (np.random.uniform(size=data_shape)).astype(dtype)
      )
      module = runtime.GraphModule(lib["default"](dev))
      module.set_input(input_name, data_tvm)
  
      # evaluate
      print("Evaluate inference time cost...")
      print(module.benchmark(dev, number=100, repeat=3))
  
file_path: tvm/gallery/how_to/tune_with_autotvm/tune_relay_x86.py
imports: import os
  import numpy
  import tvm
  from tvm import relay, autotvm
  from tvm.relay import testing
  from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
  from tvm.autotvm.graph_tuner import DPTuner, PBQPTuner
  import tvm.contrib.graph_executor
label_desc: Use image node_id 4 for calling a global function with example usage: evaluate_performance(lib, data_shape) without return types