class_docstr: Non-episodic wrapper to convert a CombinationMetaDataset into a standard
  PyTorch Dataset, compatible with (non-episodic) training.
  
  Parameters
  ----------
  dataset : `CombinationMetaDataset` instance
      The meta-dataset to be wrapped around.
  
  target_transform : callable, optional
      A function/transform that takes a target, and returns a transformed 
      version. See also `torchvision.transforms`.
code: def __len__(self):
      num_augmentations = len(self.dataset.dataset.class_augmentations)
      return self.num_samples * (num_augmentations + 1)
  
file_path: pytorch-meta/torchmeta/utils/data/wrappers.py
imports: import numpy
  import io
  from PIL import Image
  from torch.utils.data import Dataset
  from torchmeta.utils.data.dataset import CombinationMetaDataset
label_desc: Use image node_id 5 for calling the NonEpisodicWrapper obj's underlying member method code with example usage: obj.__len__() and returns: unknown