code: def _add_to_device_operation(
      transformations, device: Optional[torch.device] = None
  ):
      """
      Given a list of FFCV transformations, insert the `ToDevice` operation in the most
      appropriate place.
  
      The corrent position of the `ToDevice` operation in FFCV is very hard to infer.
      Avalanche uses the `FFCV_TRANSFORMS_DEFS` dictionary to infer the correct position
      based on the kind of input and outputs supported by the transformations.
  
      :param transformations: The list of transformations (modified in place).
      :param device: If passed, the FFCV `ToDevice` operation will be added.
      :return: The transformations with `ToDevice`.
      """
      if device is None:
          return transformations
  
      # Check if ToDevice is laready in the pipeline
      for t in transformations:
          if isinstance(t, ToDeviceFFCV):
              # Already set
              return transformations
  
      # All decoders (first operation in the pipeline) return NumPy arrays
      is_numpy = True
      is_cpu = True
  
      transformations = list(transformations)
      inserted = False
      for i, t in enumerate(transformations):
          t_def = FFCV_TRANSFORMS_DEFS.get(type(t), None)
          if t_def is None:
              # Unknown operation
              continue
  
          if is_numpy and not t_def.numpy_cpu:
              # Unmanageable situation: the current input is a NumPy array
              # but the transformation only supports PyTorch Tensor.
  
              # A warning is already raised by check_transforms_consistency,
              # so it's not a big issue...
              # Anyway, the pipeline is probably doomed to fail
              break
          elif not is_numpy:
              if not (t_def.pytorch_cpu or t_def.pytorch_gpu):
                  # Unmanageable situation: the current input is a PyTorch Tensor
                  # but the transformation only supports NumPy arrays.
  
                  # A warning is already raised by check_transforms_consistency
                  break
  
              if is_cpu and t_def.pytorch_gpu:
                  transformations.insert(i, ToDeviceFFCV(device=device))
                  inserted = True
                  break
  
              elif (not is_cpu) and t_def.pytorch_cpu:
                  # From GPU to CPU is currently unsupported
                  # Maybe in the future we can try to manage this...
                  break
  
          if isinstance(t, ToTensorFFCV):
              is_numpy = False
          elif isinstance(t, ToDeviceFFCV):
              is_cpu = t.device.type == "cpu"
  
      if not inserted:
          transformations.append(ToDeviceFFCV(device))
  
      return transformations
  
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 4 for calling a global function with example usage: _add_to_device_operation(transformations, device) and returns: transformations, transformations, transformations