code: def smooth(label, classes, eta=0.1):
      if isinstance(label, nd.NDArray):
          label = [label]
      smoothed = []
      for l in label:
          res = l.one_hot(
              classes,
              on_value=1 - eta + eta / classes,
              off_value=eta / classes,
          )
          smoothed.append(res)
      return smoothed
  
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 6 for calling a global function with example usage: smooth(label, classes, eta) and returns: smoothed