code: def _mixup_label(
      self, label: torch.Tensor, *, lam: float
  ) -> torch.Tensor:
      label = one_hot(label, num_classes=self.num_classes)
      if not label.dtype.is_floating_point:
          label = label.float()
      return label.roll(1, 0).mul_(1.0 - lam).add_(label.mul(lam))
  
imports: import math
  import numbers
  import warnings
  from typing import Any, Callable, Dict, List, Tuple
  import PIL.Image
  import torch
  from torch.nn.functional import one_hot
  from torch.utils._pytree import tree_flatten, tree_unflatten
  from torchvision import transforms, tv_tensors
  from torchvision.transforms.v2 import functional
  from ._transform import _RandomApplyTransform, Transform
  from ._utils import _parse_labels_getter, has_any, is_pure_tensor, query_chw, query_size
label_desc: Use image node_id 4 for calling the _BaseMixUpCutMix obj's underlying member method code with example usage: obj._mixup_label(label) and returns: label