text: import argparse
  import logging
  import os
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.optim as optim
  from torch.nn.parallel import DistributedDataParallel
  import ray
  from ray import tune
  from ray.tune.integration.torch import (DistributedTrainableCreator,
                                          distributed_checkpoint_dir)
  
  
  class ImageClassifier():
      def __init__(self,
                   config,
                   logger=None,
                   reporter=None,
                   net=None,
                   optimizer=None,
                   problem_type=None):
  
      def fit():
          return NotImplemented
      
      def train_loop(config, checkpoint_dir=False):
          use_cuda = torch.cuda.is_available()
          device = torch.device("cuda" if use_cuda else "cpu")
          train_loader, test_loader = get_data_loaders()
          model = ConvNet().to(device)
          optimizer = optim.SGD(model.parameters(), lr=0.1)
  
          if checkpoint_dir:
              with open(os.path.join(checkpoint_dir, "checkpoint")) as f:
                  model_state, optimizer_state = torch.load(f)
  
              model.load_state_dict(model_state)
              optimizer.load_state_dict(optimizer_state)
  
          model = DistributedDataParallel(model)
  
          for epoch in range(10):
              train(model, optimizer, train_loader, device)
              acc = test(model, test_loader, device)
  
              if epoch % 3 == 0:
                  with distributed_checkpoint_dir(step=epoch) as checkpoint_dir:
                      path = os.path.join(checkpoint_dir, "checkpoint")
                      torch.save((model.state_dict(), optimizer.state_dict()), path)
              tune.report(mean_accuracy=acc)
  
  
      def train(model, optimizer, train_loader, device=None):
          device = device or torch.device("cpu")
          model.train()
          for batch_idx, (data, target) in enumerate(train_loader):
              data, target = data.to(device), target.to(device)
              optimizer.zero_grad()
              output = model(data)
              loss = F.nll_loss(output, target)
              loss.backward()
              optimizer.step()
  
  
      def test(model, data_loader, device=None):
          device = device or torch.device("cpu")
          model.eval()
          correct = 0
          total = 0
          with torch.no_grad():
              for batch_idx, (data, target) in enumerate(data_loader):
                  if batch_idx * len(data) > TEST_SIZE:
                      break
                  data, target = data.to(device), target.to(device)
                  outputs = model(data)
                  _, predicted = torch.max(outputs.data, 1)
                  total += target.size(0)
                  correct += (predicted == target).sum().item()
  
          return correct / total
  
  
  
      def run_ddp_tune(num_workers, num_gpus_per_worker, workers_per_node=None):
          trainable_cls = DistributedTrainableCreator(
              train_mnist,
              num_workers=num_workers,
              num_gpus_per_worker=num_gpus_per_worker,
              num_workers_per_host=workers_per_node)
  
          analysis = tune.run(trainable_cls,
                              num_samples=4,
                              stop={"training_iteration": 10},
                              metric="mean_accuracy",
                              mode="max")
  
          print("Best hyperparameters found were: ", analysis.best_config)
  