class_docstr: Apply a config of a deterministic schedule.
  This is used for building a single Relay operator with deterministic schedule
  for testing schedules at Relay level.
  
  Parameters
  ----------
  tasks : list[tvm.autotvm.task.task.Task]
      List of autoTVM tasks.
  schedule_names : str, List[str]
      Name of schedules to use.
class_docstr_tok: ["Apply","a","config","of","a","deterministic","schedule",".","This","is","used","for","building","a","single","Relay","operator","with","deterministic","schedule","for","testing","schedules","at","Relay","level",".","Parameters","--","--","--","--","--","tasks",":","list","[","tvm.autotvm.task.task.Task","]","List","of","autoTVM","tasks",".","schedule_names",":","str",",","List","[","str","]","Name","of","schedules","to","use","."]
docstr: Override query
docstr_tok: ["Override","query"]
code: def _query_inside(self, target, workload):
      """Override query"""
      self.workload = workload
  
      # Create a config from correct task
      for task in self._tasks:
          if task.name == workload[0]:
              config = task.config_space.get(0)
              break
  
      if not config:
          raise RuntimeError(
              f"workload: {str(workload)} does not exist in {str(self._tasks)}"
          )
      # Add low cost to the target schedule and high cost to others.
      if workload[0] in self._schedule_names:
          config.cost = 1e-6
      else:
          config.cost = 100000
      return config
  
imports: from __future__ import absolute_import
  from io import TextIOBase
  import logging
  from os import PathLike
  from pathlib import Path
  from typing import List, Iterable, Tuple, Union
  import numpy
  from .space import FallbackConfigEntity
  from ..None import env
  from ..measure import MeasureInput, MeasureResult
label_desc: Use image node_id 2 for calling the ApplyFixedConfig obj's underlying member method code with example usage: obj._query_inside(target, workload) and returns: config