class_docstr: The class of tuning records.
  
  Parameters
  ----------
  trace : tvm.ir.Trace
      The trace of the tuning record.
  workload : Workload
      The workload of the tuning record.
  run_secs : Optional[List[float]]
      The run time of the tuning record.
  target : Optional[Target]
      The target of the tuning record.
  args_info : Optional[List[ArgInfo]]
      The argument information of the tuning record.
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docstr: Generate a measure candidate given an initial IR module and a trace
  stored in the tuning record.
  
  Returns
  -------
  candidate : MeasureCandidate
      A generated candidate.
docstr_tok: ["Generate","a","measure","candidate","given","an","initial","IR","module","and","a","trace","stored","in","the","tuning","record",".","Returns","--","--","--","-","candidate",":","MeasureCandidate","A","generated","candidate","."]
code: def as_measure_candidate(self) -> Any:
      """Generate a measure candidate given an initial IR module and a trace
      stored in the tuning record.
  
      Returns
      -------
      candidate : MeasureCandidate
          A generated candidate.
      """
      return _ffi_api.TuningRecordAsMeasureCandidate(self)
  
imports: from typing import Any, Callable, List, Optional, Union
  from typing_extensions import Literal
  from tvm._ffi import register_object
  from tvm.ir.module import IRModule
  from tvm.runtime import Object
  from tvm.target import Target
  from tvm.tir.schedule import Schedule, Trace
  from ..None import _ffi_api
  from ..arg_info import ArgInfo
  from ..utils import _json_de_tvm
label_desc: Use image node_id 2 for calling the TuningRecord obj's underlying member method code with example usage: obj.as_measure_candidate() and returns: _ffi_api