code: def mixup_data(x, y, alpha=1.0, use_cuda=True):
      """Returns mixed inputs, pairs of targets, and lambda"""
      if alpha > 0:
          lam = np.random.beta(alpha, alpha)
      else:
          lam = 1
  
      batch_size = x.size()[0]
      if use_cuda:
          index = torch.randperm(batch_size).cuda()
      else:
          index = torch.randperm(batch_size)
  
      mixed_x = lam * x + (1 - lam) * x[index, :]
      y_a, y_b = y, y[index]
      return mixed_x, y_a, y_b, lam
  
imports: import math
  import random
  import numpy
  import torch
  import torch.distributed
  from PIL import Image, ImageEnhance, ImageOps
  from torch.utils.data import Sampler
label_desc: Use image node_id 2 for calling a global function with example usage: mixup_data(x, y, alpha, use_cuda) and returns: mixed_x,  y_a,  y_b,  lam