text: """Implementation of the Operation abstract base class."""
  import json
  from abc import ABC, abstractmethod
  from typing import Callable, List
  
  from robustnessgym.core.identifier import Identifier
  from robustnessgym.core.tools import persistent_hash
  
  
  class Operation(ABC):
      """Abstract base class for operations in Robustness Gym."""
  
      def __init__(
          self,
          apply_fn: Callable = None,
          identifiers: List[Identifier] = None,
          num_outputs: int = None,
          *args,
          **kwargs
      ):
  
          if not identifiers:
              assert (
                  num_outputs
              ), "Must pass in num_outputs if no identifiers are specified."
  
          # Set the identifiers for the outputs of the Operation
          self._identifiers = (
              Identifier.range(n=num_outputs, _name=self.__class__.__name__, **kwargs)
              if not identifiers
              else identifiers
          )
  
          # Assign the apply_fn
          if apply_fn:
              self.apply = apply_fn
  
          # # Find the batch and dataset processors
          # self._batch_processors = {method.__name__ for method in
          #                           methods_with_decorator(self.__class__,
          #                           batch_processing)}
          # self._dataset_processors = {method.__name__ for method in
          #                             methods_with_decorator(self.__class__,
          #                             dataset_processing)}
  
      @property
      def identifiers(self):
          return self._identifiers
  
      # @property
      # @abstractmethod
      # def processors(self):
      #     raise NotImplementedError("Must specify the order in which processors are
      #     applied.")
      #
      # @property
      # def batch_processors(self):
      #     return self._batch_processors
      #
      # @property
      # def dataset_processors(self):
      #     return self._dataset_processors
  
      def __hash__(self):
          """Compute a hash value for the cached operation object."""
          val = 0
          for identifier in self.identifiers:
              val ^= persistent_hash(str(identifier))
          return val
  
      # def get_cache_hash(self,
      #                    columns: List[str],
      #                    processor: str = None):
      #     """
      #     Construct a hash that will be used to identify the application of a
      #     Operation to the columns of a dataset.
      #     """
      #
      #     # Hash the Operation
      #     val = hash(self)
      #
      #     # Combine with the hash for each column
      #     for column in columns:
      #         val ^= persistent_hash(column)
      #
      #     # Combine with the hash for the processor
      #     if processor:
      #         val ^= persistent_hash(processor)
      #
      #     return val
      #
      # def get_cache_file_name(self,
      #                         columns: List[str],
      #                         processor: str = None) -> str:
      #     """
      #     Construct a file name for caching.
      #     """
      #     return 'cache-' + str(abs(self.get_cache_hash(columns=columns,
      #     processor=processor))) + '.arrow'
  
      # # FIXME: temporary
      # def __call__(self,
      #              batch_or_dataset: BatchOrDataset,
      #              columns: List[str],
      #              mask: List[int] = None,
      #              *args,
      #              **kwargs) -> BatchOrDataset:
      #
      #     if isinstance(batch_or_dataset, Dataset):
      #         # Check the Dataset's InteractionTape to see if the Operation was
      #         previously applied
      #         if not mask:
      #             # This infers a mask that specifies which outputs of the Operation
      #             are not required
      #             mask = batch_or_dataset.check_tape(
      #                 path=[self.__class__.__name__],
      #                 identifiers=self.identifiers,
      #                 columns=columns
      #             )
      #
      #         # If all outputs of the Operation were previously present in the
      #         Dataset, simply return
      #         if all(mask):
      #             return batch_or_dataset
      #
      #         # Apply the CachedOperation to the dataset
      #         dataset = self.process_dataset(
      #             dataset=batch_or_dataset,
      #             columns=columns,
      #         )
      #
      #         # Update the InteractionTape with the applied CachedOperation
      #         dataset.update_tape(
      #             path=[CACHED_OPS],
      #             identifiers=self.identifiers,
      #             columns=columns,
      #         )
      #
      #         return dataset
      #
      #     elif isinstance(batch_or_dataset, Dict):
      #
      #         assert len(self.dataset_processors) == 0, \
      #             f"Cannot apply {self.__class__.__name__} to a batch, " \
      #             f"since it has dataset processors: {self.dataset_processors}. " \
      #             f"Use Dataset.from_batch(batch) before calling {
      #             self.__class__.__name__}."
      #
      #         # Apply the Operation
      #         return self.process_batch(
      #             batch=batch_or_dataset,
      #             columns=columns
      #         )
      #     else:
      #         raise NotImplementedError
      #
      # def wrap_batch_processor(self,
      #                          batch_processor: Callable) -> Callable:
      #
      #     def _wrap_batch_processor(batch: Batch,
      #                               columns: List[str],
      #                               **kwargs):
      #
      #         return batch_processor(batch=batch, columns=columns, **kwargs)
      #
      #     return _wrap_batch_processor
      #
      # def process_dataset(self,
      #                     dataset: Dataset,
      #                     columns: List[str],
      #                     batch_size: int = 32) -> Dataset:
      #     """
      #     Apply the Operation to a dataset.
      #     """
      #
      #     # Apply them in order
      #     for method in self.processors:
      #
      #         # Apply batch processors by .map(..) over the dataset
      #         if method.__name__ in self.batch_processors:
      #             dataset = dataset.map(
      #                 partial(method, columns=columns),
      #                 batched=True,
      #                 batch_size=batch_size,
      #                 cache_file_name=self.get_cache_file_name(columns=columns,
      #                 processor=method)
      #             )
      #         # Apply dataset processors directly
      #         elif method.__name__ in self.dataset_processors:
      #             dataset = method(
      #                 dataset=dataset,
      #                 columns=columns,
      #             )
      #         else:
      #             raise RuntimeError(f"{method} is not a processor. "
      #                                f"Please remove {method} from the `processors`
      #                                property or decorate it.")
      #
      #     return dataset
      #
      # def process_batch(self,
      #                   batch: Batch,
      #                   columns: List[str]) -> Batch:
      #     """
      #     Apply the cached operation to a batch.
      #     """
      #     assert len(set(columns) - set(batch.keys())) == 0, "Any column in 'columns'
      #     must be present in 'batch'."
      #
      #     # Run the cached operation, and encode outputs (defaults to json.dumps)
      #     encoded_outputs = [
      #         self.encode(example_output)
      #         for example_output in self.apply(batch=batch, columns=columns)
      #     ]
      #
      #     # Construct updates
      #     updates = self.construct_updates(
      #         encoded_outputs=encoded_outputs,
      #         columns=columns
      #     )
      #
      #     # Update the cache and return the updated batch
      #     return self.store(batch=batch, updates=updates)
  
      @classmethod
      def identify(cls, **kwargs):
          return Identifier(_name=cls.__name__, **kwargs)
  
      @classmethod
      def encode(cls, obj) -> str:
          """
  
          Args:
              obj:
  
          Returns:
  
          """
          return json.dumps(obj)
  
      @classmethod
      def decode(cls, s: str):
          """
  
          Args:
              s:
  
          Returns:
  
          """
          return json.loads(s)
  
      @abstractmethod
      def apply(self, *args, **kwargs):
          """
  
          Args:
              *args:
              **kwargs:
  
          Returns:
  
          """
          pass
  