class_docstr: Protocol definition of a Dataset that has a valid targets field (like the
  Datasets in the torchvision package) for classification.
  
  For classification purposes, the targets field must be a sequence of ints.
  describing the class label of each pattern.
  
  This class however describes a targets field as a sequence of elements
  that can be converted to `int`. The main reason for this choice is that
  the targets field of some torchvision datasets is a Tensor. This means that
  this protocol class supports both sequence of native ints and Tensor of ints
  (or longs).
  
  On the contrary, class :class:`IClassificationDataset` strictly
  defines a `targets` field as sequence of native `int`s.
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docstr: A sequence of ints or a PyTorch Tensor or a NumPy ndarray describing the
  label of each pattern contained in the dataset.
docstr_tok: ["A","sequence","of","ints","or","a","PyTorch","Tensor","or","a","NumPy","ndarray","describing","the","label","of","each","pattern","contained","in","the","dataset","."]
code: def targets(self) -> Sequence[SupportsInt]:
      """
      A sequence of ints or a PyTorch Tensor or a NumPy ndarray describing the
      label of each pattern contained in the dataset.
      """
      ...
  
imports: from typing import TypeVar, SupportsInt, Sequence
  from torch.utils.data.dataset import Dataset
  from typing_extensions import Protocol
label_desc: Use image node_id 1 for calling the ISupportedClassificationDataset obj's underlying member method code with example usage: obj.targets() without return types