code: def check_transforms_consistency(
      transformations, warn_gpu_to_cpu: bool = True
  ):
      """
      Checks if the list of transformations has issues with input/output formats
      and devices consistency:
  
      :param transformations: The list of transformations.
      :param warn_gpu_to_cpu: Warn if ToDevice is used to move tensors from the
          gpu to the cpu.
      :return: True if the list of transformations has no obvious consistency issues.
      """
      had_issues = False
  
      # All decoders (first operation in the pipeline) return NumPy arrays
      is_numpy = True
      is_cpu = True
  
      for t in transformations:
          t_def = FFCV_TRANSFORMS_DEFS.get(type(t), None)
          if t_def is None:
              # Unknown operation
              continue
  
          bad_usage_type = None
  
          if is_numpy and not t_def.numpy_cpu:
              bad_usage_type = "NumPy arrays"
          elif not is_numpy:
              if is_cpu and not t_def.pytorch_cpu:
                  bad_usage_type = "CPU PyTorch Tensors"
              elif (not is_cpu) and not t_def.pytorch_gpu:
                  bad_usage_type = "GPU PyTorch Tensors"
  
          if bad_usage_type is not None:
              warnings.warn(
                  f"Transformation {type(t)} cannot be used on "
                  f"{bad_usage_type}.\n"
                  f"Its registered definition is: {t_def}.\n"
                  f"This may lead to issues with Numba..."
              )
              had_issues = True
  
          if isinstance(t, ToTensorFFCV):
              is_numpy = False
          elif isinstance(t, ToDeviceFFCV):
              if (not is_cpu) and t.device.type == "cpu":
                  if warn_gpu_to_cpu:
                      warnings.warn(
                          f"Moving a Tensor from GPU to CPU is quite unusual..."
                      )
                      had_issues = True
  
              is_cpu = t.device.type == "cpu"
  
      return not had_issues
  
imports: from typing import Any, Callable, Dict, List, NamedTuple, Optional, Tuple, Type, Union
  from typing_extensions import Literal
  import warnings
  import numpy
  import torch
  from avalanche.benchmarks.utils.transforms import flat_transforms_recursive
  from avalanche.benchmarks.utils.ffcv_support.center_crop import CenterCropRGBImageDecoderTVAlike
  from torchvision.transforms import ToTensor
  from torchvision.transforms import PILToTensor
  from torchvision.transforms import Normalize
  from torchvision.transforms import ConvertImageDtype
  from torchvision.transforms import RandomResizedCrop
  from torchvision.transforms import CenterCrop
  from torchvision.transforms import RandomHorizontalFlip
  from torchvision.transforms import RandomCrop
  from ffcv.transforms import ToTensor
  from ffcv.transforms import ToDevice
  from ffcv.transforms import ToTorchImage
  from ffcv.transforms import NormalizeImage
  from ffcv.transforms import Convert
  from ffcv.transforms import View
  from ffcv.transforms import Squeeze
  from ffcv.transforms import RandomResizedCrop
  from ffcv.transforms import RandomHorizontalFlip
  from ffcv.transforms import RandomTranslate
  from ffcv.transforms import Cutout
  from ffcv.transforms import ImageMixup
  from ffcv.transforms import LabelMixup
  from ffcv.transforms import MixupToOneHot
  from ffcv.transforms import Poison
  from ffcv.transforms import ReplaceLabel
  from ffcv.transforms import RandomBrightness
  from ffcv.transforms import RandomContrast
  from ffcv.transforms import RandomSaturation
  from ffcv.transforms import ModuleWrapper
  from ffcv.pipeline.operation import Operation
  from ffcv.pipeline.state import State
  from ffcv.pipeline.allocation_query import AllocationQuery
  from ffcv.fields.decoders import SimpleRGBImageDecoder, RandomResizedCropRGBImageDecoder
  from dataclasses import replace
label_desc: Use image node_id 5 for calling a global function with example usage: check_transforms_consistency(transformations, warn_gpu_to_cpu) and returns: unknown