code: def test_meta_schedule_cost_model():
      @derived_object
      class FancyCostModel(PyCostModel):
          def load(self, path: str) -> None:
              pass
  
          def save(self, path: str) -> None:
              pass
  
          def update(
              self,
              context: TuneContext,
              candidates: List[MeasureCandidate],
              results: List[RunnerResult],
          ) -> None:
              pass
  
          def predict(
              self,
              context: TuneContext,
              candidates: List[MeasureCandidate],
          ) -> np.ndarray:
              return np.random.rand(10)
  
      model = FancyCostModel()
      model.save("fancy_test_location")
      model.load("fancy_test_location")
      model.update(TuneContext(), [], [])
      results = model.predict(
          TuneContext(),
          [
              MeasureCandidate(Schedule(mod=Matmul), [])
              for _ in range(10)
          ],
      )
      assert results.shape == (10,)
  
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 1 for calling a global function with example usage: test_meta_schedule_cost_model() and returns: np