class_docstr: An abstract class representing a :class:`Dataset`.
  
  All datasets that represent a map from keys to data samples should subclass
  it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
  data sample for a given key. Subclasses could also optionally overwrite
  :meth:`__len__`, which is expected to return the size of the dataset by many
  :class:`~torch.utils.data.Sampler` implementations and the default options
  of :class:`~torch.utils.data.DataLoader`. Subclasses could also
  optionally implement :meth:`__getitems__`, for speedup batched samples
  loading. This method accepts list of indices of samples of batch and returns
  list of samples.
  
  .. note::
    :class:`~torch.utils.data.DataLoader` by default constructs an index
    sampler that yields integral indices.  To make it work with a map-style
    dataset with non-integral indices/keys, a custom sampler must be provided.
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code: def __add__(self, other: "Dataset[T_co]") -> "ConcatDataset[T_co]":
      return ConcatDataset([self, other])
  
imports: import bisect
  import itertools
  import math
  import warnings
  from typing import cast, Dict, Generic, Iterable, List, Optional, Sequence, Tuple, TypeVar, Union
  from torch import default_generator, randperm
  from ...None import Generator, Tensor
label_desc: Use image node_id 2 for calling the Dataset obj's underlying member method code with example usage: obj.__add__(other) and returns: ConcatDataset