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 __init__(self, dataset, target_transform=None):
      super(NonEpisodicWrapper, self).__init__()
      if not isinstance(dataset, CombinationMetaDataset):
          raise ValueError(
              "`NonEpisodicWrapper` can only be wrapped around a "
              "`CombinationMetaDataset`. The dataset `{0}` is not an instance "
              "of `CombinationMetaDataset`.".format(dataset)
          )
  
      self.dataset = dataset
      self.target_transform = target_transform
  
      class_dataset = self.dataset.dataset
      self._labels, pointer = [], 0
      self._offsets = np.zeros(
          (class_dataset.num_classes,), dtype=np.int_
      )
      for index, label in enumerate(class_dataset.labels):
          if isinstance(label, list):
              label = "/".join(label)
          num_samples = len(class_dataset.data[label])
          self._labels.append(label)
          self._offsets[index] = pointer
          pointer += num_samples
      self._num_samples = pointer
  
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 1 to create a new NonEpisodicWrapper object from inherited base classes: Dataset with example: obj = NonEpisodicWrapper(dataset, target_transform)