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 __getitem__(self, index):
      class_dataset = self.dataset.dataset
  
      class_index = np.maximum(
          np.searchsorted(
              self._offsets, index % self.num_samples, side="left"
          )
          - 1,
          0,
      )
      offset = (index % self.num_samples) - self._offsets[class_index]
      label = self._labels[class_index]
  
      array = class_dataset.data[label][offset]
      image = (
          Image.open(io.BytesIO(array))
          if array.ndim < 2
          else Image.fromarray(array)
      )
  
      class_augmented_index = (
          class_dataset.num_classes * (index // self.num_samples)
          + class_index
      )
      transform = class_dataset.get_transform(
          class_augmented_index, class_dataset.transform
      )
      if transform is not None:
          image = transform(image)
  
      class_transform = class_dataset.get_class_augmentation(
          class_augmented_index
      )
      label = (label, index // self.num_samples)
      if self.target_transform is not None:
          label = self.target_transform(label)
  
      return image, label
  
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 2 for calling the NonEpisodicWrapper obj's underlying member method code with example usage: obj.__getitem__(index) and returns: image,  label