code: def convert_dataloader_to_fastai(dataloader: DataLoader):
      def raise_error_convert(data):
          raise NotImplementedError
  
      class FastaiDataLoaderWithCollate(fastai.DataLoader):
          def create_batch(self, b):
              return (dataloader.collate_fn, raise_error_convert)[
                  self.prebatched
              ](b)
  
      # use the type of sampler to determine if shuffle is true or false
      if isinstance(
          dataloader.sampler,
          (SequentialSampler, _InfiniteConstantSampler),
      ):
          shuffle = False
      elif isinstance(dataloader.sampler, RandomSampler):
          shuffle = True
      else:
          raise ValueError(
              f"Sampler {type(dataloader.sampler)} not supported. Fastai only"
              "supports RandomSampler or SequentialSampler"
          )
  
      return FastaiDataLoaderWithCollate(
          dataset=dataloader.dataset,
          bs=dataloader.batch_size,
          num_workers=dataloader.num_workers,
          drop_last=dataloader.drop_last,
          shuffle=shuffle,
          pin_memory=dataloader.pin_memory,
      )
  
imports: from icevision.imports import *
  from icevision.engines.fastai.imports import *
  from torch.utils.data import SequentialSampler, RandomSampler
  from torch.utils.data.dataloader import _InfiniteConstantSampler
label_desc: Use image node_id 1 for calling a global function with example usage: convert_dataloader_to_fastai(dataloader) and returns: FastaiDataLoaderWithCollate, dataloader,  raise_error_convert