code: def mixup_transform(label, classes, lam=1, eta=0.0):
      if isinstance(label, nd.NDArray):
          label = [label]
      res = []
      for l in label:
          y1 = l.one_hot(
              classes,
              on_value=1 - eta + eta / classes,
              off_value=eta / classes,
          )
          y2 = l[::-1].one_hot(
              classes,
              on_value=1 - eta + eta / classes,
              off_value=eta / classes,
          )
          res.append(lam * y1 + (1 - lam) * y2)
      return res
  
file_path: gluon-cv/gluoncv/auto/estimators/image_classification/utils.py
imports: import os
  import math
  import mxnet
  from mxnet import nd
  from mxnet import gluon
  from mxnet.gluon.data.vision import transforms
  from ....data import imagenet
label_desc: Use image node_id 5 for calling a global function with example usage: mixup_transform(label, classes, lam, eta) and returns: res