code: def forward(self, *inputs):
      inputs = inputs if len(inputs) > 1 else inputs[0]
      flat_inputs, spec = tree_flatten(inputs)
      needs_transform_list = self._needs_transform_list(flat_inputs)
  
      if has_any(
          flat_inputs,
          PIL.Image.Image,
          tv_tensors.BoundingBoxes,
          tv_tensors.Mask,
      ):
          raise ValueError(
              f"{type(self).__name__}() does not support PIL images, bounding boxes and masks."
          )
  
      labels = self._labels_getter(inputs)
      if not isinstance(labels, torch.Tensor):
          raise ValueError(
              f"The labels must be a tensor, but got {type(labels)} instead."
          )
      elif labels.ndim != 1:
          raise ValueError(
              f"labels tensor should be of shape (batch_size,) "
              f"but got shape {labels.shape} instead."
          )
  
      params = {
          "labels": labels,
          "batch_size": labels.shape[0],
          **self._get_params(
              [
                  inpt
                  for (inpt, needs_transform) in zip(
                      flat_inputs, needs_transform_list
                  )
                  if needs_transform
              ]
          ),
      }
  
      # By default, the labels will be False inside needs_transform_list, since they are a torch.Tensor coming
      # after an image or video. However, we need to handle them in _transform, so we make sure to set them to True
      needs_transform_list[
          next(
              idx
              for idx, inpt in enumerate(flat_inputs)
              if inpt is labels
          )
      ] = True
      flat_outputs = [
          self._transform(inpt, params) if needs_transform else inpt
          for (inpt, needs_transform) in zip(
              flat_inputs, needs_transform_list
          )
      ]
  
      return tree_unflatten(flat_outputs, spec)
  
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 2 for calling the _BaseMixUpCutMix obj's underlying member method code with example usage: obj.forward() and returns: tree_unflatten