code: def step(self):
      train_func(
          self.model,
          self.optimizer,
          self.train_loader,
          device=self.device,
      )
      acc = test_func(self.model, self.test_loader, self.device)
      return {"mean_accuracy": acc}
  
file_path: ray/python/ray/tune/examples/mnist_pytorch_trainable.py
imports: from __future__ import print_function
  import argparse
  import os
  import torch
  import torch.optim
  import ray
  from ray import train, tune
  from ray.tune.schedulers import ASHAScheduler
  from ray.tune.examples.mnist_pytorch import train_func, test_func, get_data_loaders, ConvNet
label_desc: Use image node_id 2 for calling the TrainMNIST obj's underlying member method code with example usage: obj.step() and returns: dict