code: def _needs_transform_list(self, flat_inputs: List[Any]) -> List[bool]:
      # Below is a heuristic on how to deal with pure tensor inputs:
      # 1. Pure tensors, i.e. tensors that are not a tv_tensor, are passed through if there is an explicit image
      #    (`tv_tensors.Image` or `PIL.Image.Image`) or video (`tv_tensors.Video`) in the sample.
      # 2. If there is no explicit image or video in the sample, only the first encountered pure tensor is
      #    transformed as image, while the rest is passed through. The order is defined by the returned `flat_inputs`
      #    of `tree_flatten`, which recurses depth-first through the input.
      #
      # This heuristic stems from two requirements:
      # 1. We need to keep BC for single input pure tensors and treat them as images.
      # 2. We don't want to treat all pure tensors as images, because some datasets like `CelebA` or `Widerface`
      #    return supplemental numerical data as tensors that cannot be transformed as images.
      #
      # The heuristic should work well for most people in practice. The only case where it doesn't is if someone
      # tries to transform multiple pure tensors at the same time, expecting them all to be treated as images.
      # However, this case wasn't supported by transforms v1 either, so there is no BC concern.
  
      needs_transform_list = []
      transform_pure_tensor = not has_any(
          flat_inputs,
          tv_tensors.Image,
          tv_tensors.Video,
          PIL.Image.Image,
      )
      for inpt in flat_inputs:
          needs_transform = True
  
          if not check_type(inpt, self._transformed_types):
              needs_transform = False
          elif is_pure_tensor(inpt):
              if transform_pure_tensor:
                  transform_pure_tensor = False
              else:
                  needs_transform = False
          needs_transform_list.append(needs_transform)
      return needs_transform_list
  
imports: from __future__ import annotations
  import enum
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
  import PIL.Image
  import torch
  from torch import nn
  from torch.utils._pytree import tree_flatten, tree_unflatten
  from torchvision import tv_tensors
  from torchvision.transforms.v2._utils import check_type, has_any, is_pure_tensor
  from torchvision.utils import _log_api_usage_once
  from .functional._utils import _get_kernel
label_desc: Use image node_id 7 for calling the Transform obj's underlying member method code with example usage: obj._needs_transform_list(flat_inputs) and returns: needs_transform_list