code: def __init__(self, infer_noise: bool = True) -> None:
      super().__init__(name="accuracy")
      self.infer_noise = infer_noise
  
imports: from typing import Any, Dict, Iterable, Set
  import pandas
  import torch
  from ax.benchmark.benchmark_problem import SingleObjectiveBenchmarkProblem
  from ax.core.base_trial import BaseTrial, TrialStatus
  from ax.core.data import Data
  from ax.core.metric import Metric, MetricFetchE, MetricFetchResult
  from ax.core.objective import Objective
  from ax.core.optimization_config import OptimizationConfig
  from ax.core.parameter import ParameterType, RangeParameter
  from ax.core.runner import Runner
  from ax.core.search_space import SearchSpace
  from ax.utils.common.base import Base
  from ax.utils.common.equality import equality_typechecker
  from ax.utils.common.result import Err, Ok
  from torch import nn, optim
  from torch.nn import functional
  from torch.utils.data import DataLoader, Dataset
label_desc: Use image node_id 1 to create a new PyTorchCNNMetric object from inherited base classes: Metric with example: obj = PyTorchCNNMetric(infer_noise)