class_docstr: Cost model.
class_docstr_tok: ["Cost","model","."]
docstr: Predict normalized score with the cost model.
  
  Parameters
  ----------
  context : TuneContext,
      The tuning context.
  candidates : List[MeasureCandidate]
      The measure candidates.
  
  Return
  ------
  result : np.ndarray
      The predicted normalized score.
docstr_tok: ["Predict","normalized","score","with","the","cost","model",".","Parameters","--","--","--","--","--","context",":","TuneContext",",","The","tuning","context",".","candidates",":","List","[","MeasureCandidate","]","The","measure","candidates",".","Return","--","--","--","result",":","np.ndarray","The","predicted","normalized","score","."]
code: def predict(
      self, context: TuneContext, candidates: List[MeasureCandidate]
  ) -> np.ndarray:
      """Predict normalized score with the cost model.
  
      Parameters
      ----------
      context : TuneContext,
          The tuning context.
      candidates : List[MeasureCandidate]
          The measure candidates.
  
      Return
      ------
      result : np.ndarray
          The predicted normalized score.
      """
      n = len(candidates)
      results = np.zeros(shape=(n,), dtype="float64")
      _ffi_api.CostModelPredict(  # type: ignore # pylint: disable=no-member
          self,
          context,
          candidates,
          results.ctypes.data_as(ctypes.c_void_p),
      )
      return results
  
imports: import ctypes
  from typing import Callable, List, Union
  from typing_extensions import Literal
  import numpy
  from tvm._ffi import register_object
  from tvm.runtime import Object
  from ..None import _ffi_api
  from ..runner import RunnerResult
  from ..search_strategy import MeasureCandidate
  from ..tune_context import TuneContext
  from ..utils import _get_default_str
label_desc: Use image node_id 4 for calling the CostModel obj's underlying member method code with example usage: obj.predict(context, candidates) and returns: results