code: def mixup_target(target, num_classes, lam=1.0, smoothing=0.0):
      """
      This function converts target class indices to one-hot vectors, given the
      number of classes.
      Args:
          targets (loader): Class labels.
          num_classes (int): Total number of classes.
          lam (float): lamba value for mixup/cutmix.
          smoothing (float): Label smoothing value.
      """
      off_value = smoothing / num_classes
      on_value = 1.0 - smoothing + off_value
      target1 = convert_to_one_hot(
          target,
          num_classes,
          on_value=on_value,
          off_value=off_value,
      )
      target2 = convert_to_one_hot(
          target.flip(0),
          num_classes,
          on_value=on_value,
          off_value=off_value,
      )
      return target1 * lam + target2 * (1.0 - lam)
  
file_path: SlowFast/slowfast/datasets/mixup.py
imports: import numpy
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: mixup_target(target, num_classes, lam, smoothing) and returns: unknown