code: def has_all(
      flat_inputs: List[Any],
      *types_or_checks: Union[Type, Callable[[Any], bool]],
  ) -> bool:
      for type_or_check in types_or_checks:
          for inpt in flat_inputs:
              if (
                  isinstance(inpt, type_or_check)
                  if isinstance(type_or_check, type)
                  else type_or_check(inpt)
              ):
                  break
          else:
              return False
      return True
  
imports: from __future__ import annotations
  import collections.abc
  import numbers
  from contextlib import suppress
  from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
  import PIL.Image
  import torch
  from torchvision import tv_tensors
  from torchvision._utils import sequence_to_str
  from torchvision.transforms.transforms import _check_sequence_input, _setup_angle, _setup_size
  from torchvision.transforms.v2.functional import get_dimensions, get_size, is_pure_tensor
  from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT
label_desc: Use image node_id 15 for calling a global function with example usage: has_all(flat_inputs) and returns: True, False