code: def tune_autotvm_tasks(tasks, log_filename):
      """Appends to log filename the best strategies for tasks"""
      if len(tasks) == 0:
          return
  
      measure_option = tvm.autotvm.measure_option(
          builder=tvm.autotvm.LocalBuilder(
              build_func=ndk.create_shared, timeout=15
          ),
          runner=tvm.autotvm.RPCRunner(
              RPC_KEY,
              host=RPC_TRACKER_HOST,
              port=RPC_TRACKER_PORT,
              number=100,
              timeout=15,
          ),
      )
  
      logging.info(
          f"Using autotvm tuning for {len(tasks)} tasks with {AUTOTVM_NUM_TRIALS} trials, logging to {log_filename}"
      )
  
      # create tmp log file, starting with contents from existing log file
      tmp_log_filename = log_filename + ".tmp"
      if os.path.exists(tmp_log_filename):
          os.remove(tmp_log_filename)
      if os.path.exists(log_filename):
          logging.info(
              f"Copying existing log {log_filename} to {tmp_log_filename}"
          )
          shutil.copy(log_filename, tmp_log_filename)
  
      for i, task in enumerate(reversed(tasks)):
          prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
          logging.info(f"Considering task {task.name} {prefix}")
          if is_already_tuned(task, tmp_log_filename):
              logging.info(f"Re-using existing record for {task.name}")
              continue
  
          logging.info(f"Using autotvm to tune {task.name}")
          tuner_obj = tvm.autotvm.tuner.XGBTuner(task, loss_type="reg")
          if os.path.exists(tmp_log_filename):
              tuner_obj.load_history(
                  tvm.autotvm.record.load_from_file(tmp_log_filename)
              )
  
          # do tuning
          n_trial = min(AUTOTVM_NUM_TRIALS, len(task.config_space))
          tuner_obj.tune(
              n_trial=n_trial,
              early_stopping=AUTOTVM_EARLY_STOPPING,
              measure_option=measure_option,
              callbacks=[
                  tvm.autotvm.callback.progress_bar(
                      n_trial, prefix=prefix
                  ),
                  tvm.autotvm.callback.log_to_file(tmp_log_filename),
              ],
          )
  
      # Pick best records and copy back to main log file
      tvm.autotvm.record.pick_best(tmp_log_filename, log_filename)
      os.remove(tmp_log_filename)
  
      logging.info("Done with autotvm tuning")
  
imports: import tvm
  import logging
  import tempfile
  import os
  import shutil
  import numpy
  from tvm.relay import testing
  from tvm import rpc
  from tvm.contrib import utils, ndk
  from tvm.relay.build_module import bind_params_by_name
  from tvm.relay.collage.collage import *
  from tvm.relay.op.contrib import clml
  import pytest
label_desc: Use image node_id 4 for calling a global function with example usage: tune_autotvm_tasks(tasks, log_filename) without return types