code: def train_and_evaluate(
      self,
      lr: float,
      momentum: float,
      weight_decay: float,
      step_size: int,
      gamma: float,
  ) -> float:
      net = self.CNN()
      net.to(device=self.device)
  
      # Train
      net.train()
      criterion = nn.NLLLoss(reduction="sum")
      optimizer = optim.SGD(
          net.parameters(),
          lr=lr,
          momentum=momentum,
          weight_decay=weight_decay,
      )
  
      scheduler = optim.lr_scheduler.StepLR(
          optimizer, step_size=step_size, gamma=gamma
      )
  
      for inputs, labels in self.train_loader:
          inputs = inputs.to(device=self.device)
          labels = labels.to(device=self.device)
  
          # zero the parameter gradients
          optimizer.zero_grad()
  
          # forward + backward + optimize
          outputs = net(inputs)
          loss = criterion(outputs, labels)
          loss.backward()
          optimizer.step()
          scheduler.step()
  
      # Evaluate
      net.eval()
      correct = 0
      total = 0
      with torch.no_grad():
          for inputs, labels in self.test_loader:
              outputs = net(inputs)
              _, predicted = torch.max(outputs.data, 1)
              total += labels.size(0)
              correct += (predicted == labels).sum().item()
  
      return correct / total
  
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 2 for calling the PyTorchCNNRunner obj's underlying member method code with example usage: obj.train_and_evaluate(lr, momentum, weight_decay, step_size, gamma) and returns: unknown