IterableDataset on multiple files

I have 1000 files, each file is processed with sliding window to generate many training sample.
I write this iterable-style dataset. Everything is working, the only thing is each batch consists of data from 1 file. Is there a way to implement a custom collate function or something similar to merge data from different workers?

class TextFileDataset(IterableDataset):
              def __init__(self, file_pathes: List,) -> None:
                  self.file_pathes = file_pathes
                  self.file_id_map= {file_path: idx for idx,
                                      file_path in enumerate(iterable=file_pathes)}
                 def process_file(self, file_path) -> None:
                       doc_indices = self.load_data(file_path=file_path)
                       idx= self.file_id_map[file_path]
                       for i in range(len(doc_indices)):
                           # run a sliding window
                           yield {"idx": torch.tensor(idx), "text1": torch.tensor(text1), "text2": torch.tensor(text2)}
              def __iter__(self) -> None:
                  worker_info =
                  if worker_info is None:
                      for file_path in self.file_pathes:
                          yield from self.process_file(file_path=file_path)
                  per_worker = int(np.ceil(len(self.file_pathes) /
                  worker_id =
                  self.iter_file_paths = self.file_pathes[worker_id *
                                                          per_worker:(worker_id + 1) * per_worker]
                  for file_path in self.iter_file_paths:
                      yield from self.process_file(file_path=file_path)