class_docstr: Avalanche Dataset.
  
  Avlanche dataset are pytorch-compatible Datasets with some additional
  functionality such as:
  - management of transformation groups via :class:`AvalancheTransform`
  - support for sample attributes such as class targets and task labels
  
  Data Attributes
  ---------------
  
  Avalanche datasets manage sample-wise information such as class or task
  labels via :class:`DataAttribute`.
  
  Transformation Groups
  ---------------------
  
  Avalanche datasets manage transformation via transformation groups.
  Simply put, a transformation group is a named preprocessing function
  (as in torchvision datasets). By default, Avalanche expects
  two transformation groups:
  - 'train', which contains transformations applied to training patterns.
  - 'eval', that contain transformations applied to test patterns.
  
  Having both groups allows to use different transformations during training
  and evaluation and to seamlessly switch between them by using the
  :func:`train` and :func:`eval` methods. Arbitrary transformation groups
  can be added and used.  If you define custom groups, you can use them by
  calling the `:func:with_transforms` method.
  
  switching to a different transformation group by calling the ``train()``,
  ``eval()`` or ``with_transforms` methods always returns a new dataset,
  levaing the original one unchanged.
  
  Ttransformation groups can be manipulated by removing, freezing, or
  replacing transformations. Each operation returns a new dataset, leaving
  the original one unchanged.
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docstr: Returns a new dataset with the transformations of the 'eval' group
  loaded.
  
  Eval transformations usually don't contain augmentation procedures.
  This function may be useful when in need to test on training data
  (for instance, in order to run a validation pass).
  
  The current dataset will not be affected.
  
  :return: A new dataset with the eval transformations loaded.
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code: def eval(self):
      """
      Returns a new dataset with the transformations of the 'eval' group
      loaded.
  
      Eval transformations usually don't contain augmentation procedures.
      This function may be useful when in need to test on training data
      (for instance, in order to run a validation pass).
  
      The current dataset will not be affected.
  
      :return: A new dataset with the eval transformations loaded.
      """
      return self.with_transforms("eval")
  
imports: import copy
  import warnings
  import numpy
  from torch.utils.data.dataloader import default_collate
  from avalanche.benchmarks.utils.dataset_definitions import IDataset
  from .data_attribute import DataAttribute
  from typing import Dict, List, Any, Optional, Sequence, TypeVar, Callable, Union, overload
  from .flat_data import FlatData
  from .transform_groups import TransformGroups, EmptyTransformGroups
  from torch.utils.data import Dataset
  from collections import OrderedDict
label_desc: Use image node_id 15 for calling the AvalancheDataset obj's underlying member method code with example usage: obj.eval() and returns: self