code: def test_meta_schedule_random_model():
      model = RandomModel()
      model.update(TuneContext(), [], [])
      res = model.predict(
          TuneContext(),
          [MeasureCandidate(Schedule(Matmul), []) for i in range(10)],
      )
      assert len(res) == 10
      assert min(res) >= 0 and max(res) <= model.max_range
  
imports: import os
  import re
  import shutil
  import tempfile
  import unittest
  from functools import partial
  from typing import List
  import numpy
  import tvm
  import tvm.testing
  from tvm.meta_schedule.cost_model import PyCostModel, RandomModel, XGBModel
  from tvm.meta_schedule.cost_model.xgb_model import PackSum, _get_custom_call_back
  from tvm.meta_schedule.feature_extractor import RandomFeatureExtractor
  from tvm.meta_schedule.runner import RunnerResult
  from tvm.meta_schedule.search_strategy import MeasureCandidate
  from tvm.meta_schedule.tune_context import TuneContext
  from tvm.meta_schedule.utils import derived_object
  from tvm.script import tir
  from tvm.tir.schedule.schedule import Schedule
label_desc: Use image node_id 3 for calling a global function with example usage: test_meta_schedule_random_model() without return types