class_docstr: A convenience class to access cached time series datasets.
  
  Note, that these *cached datasets* are statically included into *tslearn*
  and are distinct from the ones in :class:`UCR_UEA_datasets`.
  
  When using the Trace dataset, please cite [1]_.
  
  See Also
  --------
  UCR_UEA_datasets : Provides more datasets and supports caching.
  
  References
  ----------
  .. [1] A. Bagnall, J. Lines, W. Vickers and E. Keogh, The UEA & UCR Time
     Series Classification Repository, www.timeseriesclassification.com
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docstr: List cached datasets.
  
  Examples
  --------
  >>> from tslearn.datasets import UCR_UEA_datasets
  >>> _ = UCR_UEA_datasets().load_dataset("Trace")
  >>> cached = UCR_UEA_datasets().list_cached_datasets()
  >>> "Trace" in cached
  True
  
  Returns
  -------
  list of str:
      A list of names of all cached (univariate and multivariate) dataset
      namas.
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code: def list_datasets(self):
      """List cached datasets.
  
      Examples
      --------
      >>> from tslearn.datasets import UCR_UEA_datasets
      >>> _ = UCR_UEA_datasets().load_dataset("Trace")
      >>> cached = UCR_UEA_datasets().list_cached_datasets()
      >>> "Trace" in cached
      True
  
      Returns
      -------
      list of str:
          A list of names of all cached (univariate and multivariate) dataset
          namas.
      """
      return [
          fname[: fname.rfind(".")]
          for fname in os.listdir(self.path)
          if fname.endswith(".npz")
      ]
  
imports: import numpy
  import os
label_desc: Use image node_id 2 for calling the CachedDatasets obj's underlying member method code with example usage: obj.list_datasets() and returns: unknown