code: def get_indices_task(self, task):
      all_class_indices = self._get_class_indices(task)
      indices = OrderedDict([(split, []) for split in self.splits])
  
      for i, (name, class_indices) in enumerate(
          all_class_indices.items()
      ):
          num_samples = len(class_indices)
          if num_samples < self._min_samples_per_class:
              raise ValueError(
                  "The number of samples for class `{0}` ({1}) "
                  "is smaller than the minimum number of samples per class "
                  "required by `ClassSplitter` ({2}).".format(
                      name, num_samples, self._min_samples_per_class
                  )
              )
  
          if self.shuffle:
              seed = (hash(task) + i + self.random_state_seed) % (
                  2**32
              )
              dataset_indices = np.random.RandomState(seed).permutation(
                  num_samples
              )
          else:
              dataset_indices = np.arange(num_samples)
  
          ptr = 0
          for split, num_split in self.splits.items():
              split_indices = dataset_indices[ptr : ptr + num_split]
              if self.shuffle:
                  self.np_random.shuffle(split_indices)
              indices[split].extend(
                  [class_indices[idx] for idx in split_indices]
              )
              ptr += num_split
  
      return indices
  
imports: import torch
  import numpy
  from collections import OrderedDict, defaultdict
  from torchmeta.utils.data.task import Task, ConcatTask, SubsetTask
  from torchmeta.transforms.utils import apply_wrapper
label_desc: Use image node_id 2 for calling the ClassSplitter_ obj's underlying member method code with example usage: obj.get_indices_task(task) and returns: indices