code: def __init__(
      self, name: str, train_set: Dataset, test_set: Dataset
  ) -> None:
      self.name = name
  
      # pyre-fixme[4]: Attribute must be annotated.
      self.train_loader = DataLoader(train_set)
      # pyre-fixme[4]: Attribute must be annotated.
      self.test_loader = DataLoader(test_set)
  
      self.results: Dict[int, float] = {}
      self.statuses: Dict[int, TrialStatus] = {}
  
      self.device = torch.device(
          "cuda" if torch.cuda.is_available() else "cpu"
      )
  
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 PyTorchCNNRunner object from inherited base classes: Runner with example: obj = PyTorchCNNRunner(name, train_set, test_set)