code: def __eq__(self, other: Base) -> bool:
      if not isinstance(other, PyTorchCNNBenchmarkProblem):
          return False
  
      # Checking the whole datasets' equality here would be too expensive to be
      # worth it; just check names instead
      return self.name == other.name
  
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 for calling the PyTorchCNNBenchmarkProblem obj's underlying member method code with example usage: obj.__eq__(other) and returns: unknown, False