code: def mixup(alpha, data, target):
      with torch.no_grad():
          bs = data.size(0)
          c = np.random.beta(alpha, alpha)
  
          perm = torch.randperm(bs).cuda()
  
          md = c * data + (1 - c) * data[perm, :]
          mt = c * target + (1 - c) * target[perm, :]
          return md, mt
  
file_path: DALI/docs/examples/use_cases/pytorch/efficientnet/image_classification/mixup.py
imports: import torch
  import torch.nn
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: mixup(alpha, data, target) and returns: md,  mt