code: def get_single_slice_specs(
      slicing_spec: SlicingSpec, num_classes: Optional[int] = None
  ) -> List[SingleSliceSpec]:
      """Returns slices of data according to slicing_spec.
  
      Args:
        slicing_spec: the slicing specification
        num_classes: number of classes of the examples. Required when slicing by
          class.
  
      Returns:
        Slices of data according to the slicing specification.
  
      Raises:
        ValueError: If the number of slices is above `_MAX_NUM_OF_SLICES` when
          slicing by class or slicing with custom indices. Or, if `num_classes` is
          not provided when slicing by class.
      """
      result = []
  
      if slicing_spec.entire_dataset:
          result.append(SingleSliceSpec())
  
      # Create slices by class.
      by_class = slicing_spec.by_class
      if isinstance(by_class, bool):
          if by_class:
              if not num_classes:
                  raise ValueError(
                      "When by_class == True, num_classes should be given."
                  )
              if not 0 <= num_classes <= _MAX_NUM_OF_SLICES:
                  raise ValueError(
                      f"Too many classes for slicing by classes. "
                      f"Found {num_classes}."
                      f"Should be no more than {_MAX_NUM_OF_SLICES}."
                  )
              for c in range(num_classes):
                  result.append(
                      SingleSliceSpec(SlicingFeature.CLASS, c)
                  )
      elif isinstance(by_class, int):
          result.append(SingleSliceSpec(SlicingFeature.CLASS, by_class))
      elif isinstance(by_class, abc.Iterable):
          for c in by_class:
              result.append(SingleSliceSpec(SlicingFeature.CLASS, c))
  
      # Create slices by percentiles
      if slicing_spec.by_percentiles:
          for percent in range(0, 100, 10):
              result.append(
                  SingleSliceSpec(
                      SlicingFeature.PERCENTILE, (percent, percent + 10)
                  )
              )
  
      # Create slices by correctness of the classifications.
      if slicing_spec.by_classification_correctness:
          result.append(
              SingleSliceSpec(SlicingFeature.CORRECTLY_CLASSIFIED, True)
          )
          result.append(
              SingleSliceSpec(
                  SlicingFeature.CORRECTLY_CLASSIFIED, False
              )
          )
  
      # Create slices by custom indices.
      if slicing_spec.all_custom_train_indices:
          for custom_train_indices, custom_test_indices in zip(
              slicing_spec.all_custom_train_indices,
              slicing_spec.all_custom_test_indices,
          ):
              groups = np.intersect1d(
                  np.unique(custom_train_indices),
                  np.unique(custom_test_indices),
                  assume_unique=True,
              )
              if not 0 <= groups.size <= _MAX_NUM_OF_SLICES:
                  raise ValueError(
                      f"Too many groups ({groups.size}) for slicing by custom indices. "
                      f"Should be no more than {_MAX_NUM_OF_SLICES}."
                  )
              for g in groups:
                  group_name = None
                  if slicing_spec.custom_slices_names is not None:
                      if g not in slicing_spec.custom_slices_names:
                          raise ValueError(
                              f"Custom slice={g} is not in custom_slices_names"
                          )
                      group_name = slicing_spec.custom_slices_names[g]
  
                  result.append(
                      SingleSliceSpec(
                          SlicingFeature.CUSTOM,
                          (
                              custom_train_indices,
                              custom_test_indices,
                              g,
                              group_name,
                          ),
                      )
                  )
      return result
  
file_path: privacy/tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/dataset_slicing.py
imports: from collections import abc
  import copy
  import logging
  from typing import List, Optional
  import numpy
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackInputData
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import SingleSliceSpec
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import SlicingFeature
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import SlicingSpec
label_desc: Use image node_id 8 for calling a global function with example usage: get_single_slice_specs(slicing_spec, num_classes) and returns: result