class_docstr: The multiomics data for creating graph dataset.
  See `here <https://pytorch-geometric.readthedocs.io/en/latest/tutorial/create_dataset.html>`__ in PyTorch Geometric
  for the accompanying tutorial.
  
  Args:
      root (string): Root directory where the dataset should be saved.
      num_modalities (int): The total number of modalities in the dataset.
      num_classes (int): The total number of classes in the dataset.
      url (string, optional): The url to download the dataset from.
      raw_file_names (list[callable], optional): The name of the files in the ``self.raw_dir`` folder that must be
          present in order to skip downloading.
      random_split (bool, optional): Whether to split the dataset into random train and test subsets. (default:
          ``False``)
      train_size (float, optional): The proportion of the dataset to include in the train split that should be between
          0.0 and 1.0. This parameter is used when ``random_split`` is ``True``.
      transform (callable, optional): A function/transform that takes in an array_like data and returns a transformed
          version. The data object will be transformed before every access. (default: ``None``)
      pre_transform (callable, optional): A function/transform that takes in an array_like data and returns a
          transformed version. The data object will be transformed before being saved to disk. (default: ``None``)
      target_pre_transform (callable, optional): A function/transform that takes in an array_like of labels and
          returns a transformed version. The label object will be transformed before being saved to disk. (default:
          ``None``)
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docstr: Returns the number of classes in the dataset.
docstr_tok: ["Returns","the","number","of","classes","in","the","dataset","."]
code: def num_classes(self) -> int:
      r"""Returns the number of classes in the dataset."""
      return self._num_classes
  
imports: import os.path
  from typing import Callable, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from torch_geometric.data import Data, Dataset, download_url, extract_zip
  from torch_sparse import SparseTensor
  from kale.utils.distance import calculate_distance, DistanceMetric
label_desc: Use image node_id 14 for calling the MultiomicsDataset obj's underlying member method code with example usage: obj.num_classes() and returns: self