code: def main(
      ways=5,
      shots=5,
      meta_lr=0.003,
      fast_lr=0.5,
      meta_batch_size=32,
      adaptation_steps=1,
      num_iterations=60000,
      cuda=True,
      seed=42,
  ):
      local_rank = int(os.environ["LOCAL_RANK"])
      world_size = int(os.environ["WORLD_SIZE"])
      os.environ["MASTER_ADDR"] = "127.0.0.1"
      os.environ["MASTER_PORT"] = "12345"
      dist.init_process_group(
          "gloo", rank=local_rank, world_size=world_size
      )
      rank = dist.get_rank()
  
      meta_batch_size = meta_batch_size // world_size
      seed = seed + rank
  
      random.seed(seed)
      torch.manual_seed(seed)
      device = torch.device("cpu")
      if cuda and torch.cuda.device_count():
          torch.cuda.manual_seed(seed)
          device_id = rank % torch.cuda.device_count()
          device = torch.device("cuda:" + str(device_id))
  
      # Create Tasksets using the benchmark interface
      tasksets = l2l.vision.benchmarks.get_tasksets(
          # 'mini-imagenet' works too, but you need to download it manually due to license restrictions of ImageNet
          "omniglot",
          train_ways=ways,
          train_samples=2 * shots,
          test_ways=ways,
          test_samples=2 * shots,
          num_tasks=20000,
          root="data",
      )
  
      # Create model
      # model = l2l.vision.models.MiniImagenetCNN(ways)
      model = l2l.vision.models.OmniglotFC(28**2, ways)
      model.to(device)
      maml = l2l.algorithms.MAML(model, lr=fast_lr, first_order=False)
      optimizer = torch.optim.Adam(maml.parameters(), meta_lr)
      optimizer = cherry.optim.Distributed(
          maml.parameters(), opt=optimizer, sync=1
      )
      optimizer.sync_parameters()
      loss = torch.nn.CrossEntropyLoss(reduction="mean")
  
      for iteration in range(num_iterations):
          optimizer.zero_grad()
          meta_train_error = 0.0
          meta_train_accuracy = 0.0
          meta_valid_error = 0.0
          meta_valid_accuracy = 0.0
          for task in range(meta_batch_size):
              # Compute meta-training loss
              learner = maml.clone()
              batch = tasksets.train.sample()
              evaluation_error, evaluation_accuracy = fast_adapt(
                  batch,
                  learner,
                  loss,
                  adaptation_steps,
                  shots,
                  ways,
                  device,
              )
              evaluation_error.backward()
              meta_train_error += evaluation_error.item()
              meta_train_accuracy += evaluation_accuracy.item()
  
              # Compute meta-validation loss
              learner = maml.clone()
              batch = tasksets.validation.sample()
              evaluation_error, evaluation_accuracy = fast_adapt(
                  batch,
                  learner,
                  loss,
                  adaptation_steps,
                  shots,
                  ways,
                  device,
              )
              meta_valid_error += evaluation_error.item()
              meta_valid_accuracy += evaluation_accuracy.item()
  
          # Print some metrics
          if rank == 0:
              print("\n")
              print("Iteration", iteration)
              print(
                  "Meta Train Error", meta_train_error / meta_batch_size
              )
              print(
                  "Meta Train Accuracy",
                  meta_train_accuracy / meta_batch_size,
              )
              print(
                  "Meta Valid Error", meta_valid_error / meta_batch_size
              )
              print(
                  "Meta Valid Accuracy",
                  meta_valid_accuracy / meta_batch_size,
              )
  
          # Average the accumulated gradients and optimize
          for p in maml.parameters():
              p.grad.data.mul_(1.0 / meta_batch_size)
          optimizer.step()  # averages gradients across all workers
  
      meta_test_error = 0.0
      meta_test_accuracy = 0.0
      for task in range(meta_batch_size):
          # Compute meta-testing loss
          learner = maml.clone()
          batch = tasksets.test.sample()
          evaluation_error, evaluation_accuracy = fast_adapt(
              batch,
              learner,
              loss,
              adaptation_steps,
              shots,
              ways,
              device,
          )
          meta_test_error += evaluation_error.item()
          meta_test_accuracy += evaluation_accuracy.item()
      print("Meta Test Error", meta_test_error / meta_batch_size)
      print("Meta Test Accuracy", meta_test_accuracy / meta_batch_size)
  
file_path: pytorch-lightning/examples/fabric/meta_learning/train_torch.py
imports: import os
  import random
  import cherry
  import learn2learn
  import torch
  import torch.distributed
label_desc: Use image node_id 3 for calling a global function with example usage: main(ways, shots, meta_lr, fast_lr, meta_batch_size, adaptation_steps, num_iterations, cuda, seed) without return types