class_docstr: Load the graph level tuning optimal schedules.
  
  The input records should be in the ascending order of
  node index for target operator. Usually this can be obtained
  with graph tuner.
  
  This context maintains an internal counter to indicate the current
  node index.
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docstr: Parameters
  ----------
  records : str or iterator of (autotvm.measure.MeasureInput, autotvm.measure.MeasureResult)
      Collection of tuning records.
      If is str, then it should be the filename of a records log file.
             Each row of this file is an encoded record pair.
      Otherwise, it is an iterator.
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code: def __init__(self, records: Records):
      """
      Parameters
      ----------
      records : str or iterator of (autotvm.measure.MeasureInput, autotvm.measure.MeasureResult)
          Collection of tuning records.
          If is str, then it should be the filename of a records log file.
                 Each row of this file is an encoded record pair.
          Otherwise, it is an iterator.
      """
      # pylint: disable=import-outside-toplevel
      from ..record import load_from_file, load_from_buffer
  
      super(ApplyGraphBest, self).__init__()
      if isinstance(records, (str, bytes, PathLike)):
          records = load_from_file(records)
      elif isinstance(records, TextIOBase):
          records = load_from_buffer(records)
      else:
          records = list(records)
  
      self._records = list(records)
      self._counter = 0
      self._global_cfg_dict = {}
  
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 1 to create a new ApplyGraphBest object from inherited base classes: DispatchContext with example: obj = ApplyGraphBest(records)