class_docstr: Base class for all datasets.
  
  All datasets inherit from this class, be they stored in a file or generated on the fly.
  
  Parameters
  ----------
  task
      Type of task the dataset is meant for. Should be one of the following:
  
          - "Regression"
          - "Binary classification"
          - "Multi-class classification"
          - "Multi-output binary classification"
          - "Multi-output regression"
  n_features
      Number of features in the dataset.
  n_samples
      Number of samples in the dataset.
  n_classes
      Number of classes in the dataset, only applies to classification datasets.
  n_outputs
      Number of outputs the target is made of, only applies to multi-output datasets.
  sparse
      Whether the dataset is sparse or not.
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code: def __repr__(self):
      l_len = max(map(len, self._repr_content.keys()))
      r_len = max(map(len, self._repr_content.values()))
  
      out = f"{self.desc}\n\n" + "\n".join(
          k.rjust(l_len) + "  " + v.ljust(r_len)
          for k, v in self._repr_content.items()
      )
  
      if "Parameters\n    ----------" in self.__doc__:
          params = re.split(
              r"\w+\n\s{4}\-{3,}",
              re.split("Parameters\n    ----------", self.__doc__)[1],
          )[0].rstrip()
          out += f"\n\nParameters\n----------{params}"
  
      return out
  
imports: from __future__ import annotations
  import abc
  import inspect
  import itertools
  import os
  import pathlib
  import re
  import shutil
  import tarfile
  import typing
  import zipfile
  from urllib import request
  from river import utils
label_desc: Use image node_id 6 for calling the Dataset obj's underlying member method code with example usage: obj.__repr__() and returns: out