class_docstr: Base class for a meta-dataset, where the classification tasks are over 
  multiple classes from a `ClassDataset`.
  
  Parameters
  ----------
  dataset : `ClassDataset` instance
      A dataset of classes. Each item of `dataset` is a dataset, containing 
      all the examples from the same class.
  
  num_classes_per_task : int
      Number of classes per tasks. This corresponds to `N` in `N-way` 
      classification.
  
  target_transform : callable, optional
      A function/transform that takes a target, and returns a transformed 
      version. See also `torchvision.transforms`.
  
  dataset_transform : callable, optional
      A function/transform that takes a dataset (ie. a task), and returns a 
      transformed version of it. E.g. `transforms.ClassSplitter()`.
code: def __len__(self):
      num_classes, length = len(self.dataset), 1
      for i in range(1, self.num_classes_per_task + 1):
          length *= (num_classes - i + 1) / i
  
      if length > sys.maxsize:
          warnings.warn(
              "The number of possible tasks in {0} is "
              "combinatorially large (equal to C({1}, {2})), and exceeds "
              "machine precision. Setting the length of the dataset to the "
              "maximum integer value, which undervalues the actual number of "
              "possible tasks in the dataset. Therefore the value returned by "
              "`len(dataset)` should not be trusted as being representative "
              "of the true number of tasks.".format(
                  self, len(self.dataset), self.num_classes_per_task
              ),
              UserWarning,
              stacklevel=2,
          )
          length = sys.maxsize
      return int(length)
  
imports: import sys
  import numpy
  import warnings
  from copy import deepcopy
  from itertools import combinations
  from ordered_set import OrderedSet
  from torchvision.transforms import Compose
  from torchmeta.utils.data.task import ConcatTask
  from torchmeta.transforms import FixedCategory, Categorical, DefaultTargetTransform
  from torchmeta.transforms.utils import wrap_transform
label_desc: Use image node_id 6 for calling the CombinationMetaDataset obj's underlying member method code with example usage: obj.__len__() and returns: int