class_docstr: A special kind of evaluator that does not run real training, but queries a database.
docstr: Execute the model with the sample.
docstr_tok: ["Execute","the","model","with","the","sample","."]
code: def _execute(self, model: ExecutableModelSpace) -> Any:
      """Execute the model with the sample."""
  
      from .space import BenchmarkModelSpace
  
      if not isinstance(model, BenchmarkModelSpace):
          warnings.warn(
              "It would be better to use BenchmarkModelSpace for benchmarking to avoid "
              "unnecessary overhead and silent mistakes."
          )
      if model.sample is None:
          raise ValueError(
              "Model can not be evaluted because it has not been sampled yet."
          )
  
      return self.evaluate(model.sample)
  
imports: from __future__ import annotations
  import itertools
  import logging
  import random
  import warnings
  from typing import Any, cast
  import nni
  from nni.mutable import Sample, Mutable, LabeledMutable, Categorical, CategoricalMultiple, label_scope
  from nni.nas.evaluator import Evaluator
  from nni.nas.space import ExecutableModelSpace
  from .space import SlimBenchmarkSpace
label_desc: Use image node_id 4 for calling the BenchmarkEvaluator obj's underlying member method code with example usage: obj._execute(model) and returns: self