code: def run(self, trial: BaseTrial) -> Dict[str, Any]:
      self.statuses[trial.index] = TrialStatus.RUNNING
  
      self.statuses[trial.index] = TrialStatus.COMPLETED
      return {
          "accuracy": {
              arm.name: self.train_and_evaluate(
                  lr=arm.parameters["lr"],  # pyre-ignore[6]
                  momentum=arm.parameters["momentum"],  # pyre-ignore[6]
                  weight_decay=arm.parameters[
                      "weight_decay"
                  ],  # pyre-ignore[6]
                  step_size=arm.parameters[
                      "step_size"
                  ],  # pyre-ignore[6]
                  gamma=arm.parameters["gamma"],  # pyre-ignore[6]
              )
              for arm in trial.arms
          }
      }
  
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 3 for calling the PyTorchCNNRunner obj's underlying member method code with example usage: obj.run(trial) and returns: dict