code: def train_cifar10(
      DIST=False,
      local_rank=None,
      world_size=None,
      nccl_id=None,
      partial_update=False,
  ):
      # Define the hyperparameters for the train_cifar10
      sgd = opt.SGD(lr=0.005, momentum=0.9, weight_decay=1e-5)
      max_epoch = 5
      batch_size = 32
  
      train_x, train_y = load_train_data()
      test_x, test_y = load_test_data()
      train_x, test_x = normalize_for_resnet(train_x, test_x)
      IMG_SIZE = 224
      num_classes = 10
  
      if DIST:
          # For distributed GPU training
          sgd = opt.DistOpt(
              sgd,
              nccl_id=nccl_id,
              local_rank=local_rank,
              world_size=world_size,
          )
          dev = device.create_cuda_gpu_on(sgd.local_rank)
  
          # Dataset partition for distributed training
          train_x, train_y = data_partition(
              train_x, train_y, sgd.global_rank, sgd.world_size
          )
          test_x, test_y = data_partition(
              test_x, test_y, sgd.global_rank, sgd.world_size
          )
          world_size = sgd.world_size
      else:
          # For single GPU
          dev = device.create_cuda_gpu()
          world_size = 1
  
      from resnet import resnet50
  
      model = resnet50(num_classes=num_classes)
  
      tx = tensor.Tensor(
          (batch_size, 3, IMG_SIZE, IMG_SIZE), dev, tensor.float32
      )
      ty = tensor.Tensor((batch_size,), dev, tensor.int32)
      num_train_batch = train_x.shape[0] // batch_size
      num_test_batch = test_x.shape[0] // batch_size
      idx = np.arange(train_x.shape[0], dtype=np.int32)
  
      if DIST:
          # Synchronize the initial parameters
          autograd.training = True
          x = np.random.randn(batch_size, 3, IMG_SIZE, IMG_SIZE).astype(
              np.float32
          )
          y = np.zeros(shape=(batch_size,), dtype=np.int32)
          tx.copy_from_numpy(x)
          ty.copy_from_numpy(y)
          out = model(tx)
          loss = autograd.softmax_cross_entropy(out, ty)
          param = []
          for p, _ in autograd.backward(loss):
              synchronize(p, sgd)
              param.append(p)
  
      for epoch in range(max_epoch):
          start_time = time.time()
          np.random.shuffle(idx)
  
          if (DIST == False) or (sgd.global_rank == 0):
              print("Starting Epoch %d:" % (epoch))
  
          # Training phase
          autograd.training = True
          train_correct = np.zeros(shape=[1], dtype=np.float32)
          test_correct = np.zeros(shape=[1], dtype=np.float32)
          train_loss = np.zeros(shape=[1], dtype=np.float32)
  
          for b in range(num_train_batch):
              x = train_x[idx[b * batch_size : (b + 1) * batch_size]]
              x = augmentation(x, batch_size)
              x = resize_dataset(x, IMG_SIZE)
              y = train_y[idx[b * batch_size : (b + 1) * batch_size]]
              tx.copy_from_numpy(x)
              ty.copy_from_numpy(y)
              out = model(tx)
              loss = autograd.softmax_cross_entropy(out, ty)
              train_correct += accuracy(
                  tensor.to_numpy(out), to_categorical(y, num_classes)
              ).astype(np.float32)
              train_loss += tensor.to_numpy(loss)[0]
              if not partial_update:
                  sgd.backward_and_update(loss)
              else:
                  sgd.backward_and_partial_update(loss)
  
          if DIST:
              # Reduce the evaluation accuracy and loss from multiple devices
              reducer = tensor.Tensor((1,), dev, tensor.float32)
              train_correct = reduce_variable(
                  train_correct, sgd, reducer
              )
              train_loss = reduce_variable(train_loss, sgd, reducer)
  
          # Output the training loss and accuracy
          if (DIST == False) or (sgd.global_rank == 0):
              print(
                  "Training loss = %f, training accuracy = %f"
                  % (
                      train_loss,
                      train_correct
                      / (num_train_batch * batch_size * world_size),
                  ),
                  flush=True,
              )
  
          if partial_update:
              # Synchronize parameters before evaluation phase
              for p in param:
                  synchronize(p, sgd)
  
          # Evaluation phase
          autograd.training = False
          for b in range(num_test_batch):
              x = test_x[b * batch_size : (b + 1) * batch_size]
              x = resize_dataset(x, IMG_SIZE)
              y = test_y[b * batch_size : (b + 1) * batch_size]
              tx.copy_from_numpy(x)
              ty.copy_from_numpy(y)
              out_test = model(tx)
              test_correct += accuracy(
                  tensor.to_numpy(out_test),
                  to_categorical(y, num_classes),
              )
  
          if DIST:
              # Reduce the evaluation accuracy from multiple devices
              test_correct = reduce_variable(test_correct, sgd, reducer)
  
          # Output the evaluation accuracy
          if (DIST == False) or (sgd.global_rank == 0):
              print(
                  "Evaluation accuracy = %f, Elapsed Time = %fs"
                  % (
                      test_correct
                      / (num_test_batch * batch_size * world_size),
                      time.time() - start_time,
                  ),
                  flush=True,
              )
  
imports: from singa import singa_wrap
  from singa import autograd
  from singa import tensor
  from singa import device
  from singa import opt
  from PIL import Image
  import numpy
  import os
  import sys
  import time
label_desc: Use image node_id 13 for calling a global function with example usage: train_cifar10(DIST, local_rank, world_size, nccl_id, partial_update) without return types