class_docstr: A convenience class to access UCR/UEA time series datasets.
  
  When using one (or several) of these datasets in research projects, please
  cite [1]_.
  
  This class will attempt to recover from some known misnamed files, like the
  `StarLightCurves` dataset being provided in `StarlightCurves.zip` and
  alike.
  
  Parameters
  ----------
  use_cache : bool (default: True)
      Whether a cached version of the dataset should be used in
      :meth:`~load_dataset`, if one is found.
      Datasets are always cached upon loading, and this parameter only
      determines whether the cached version shall be refreshed upon loading.
  
  Notes
  -----
      Downloading dataset files can be time-consuming, it is recommended
      using `use_cache=True` (default) in order to only experience
      downloading time once per dataset and work on a cached version of the
      datasets afterward.
  
  See Also
  --------
  CachedDatasets : Provides pre-selected datasets for offline use.
  
  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: Determines whether some downloaded and unzipped dataset provides
  both training and test data in the given format.
  
  Parameters
  ----------
  dataset_name : str
      the name of the dataset
  ext : str or None
      the file extension without a dot, e.g `"txt"` or `"arff"`;
      if set to None (the default), `True` will be returned if either TXT
      or ARFF files are present
  
  Returns
  -------
  bool
      if there are both training and test files with the specified
      file extension
docstr_tok: ["Determines","whether","some","downloaded","and","unzipped","dataset","provides","both","training","and","test","data","in","the","given","format",".","Parameters","--","--","--","--","--","dataset_name",":","str","the","name","of","the","dataset","ext",":","str","or","None","the","file","extension","without","a","dot",",","e.g","`","``","txt","''","`","or","`","``","arff","''","`",";","if","set","to","None","(","the","default",")",",","`","True","`","will","be","returned","if","either","TXT","or","ARFF","files","are","present","Returns","--","--","--","-","bool","if","there","are","both","training","and","test","files","with","the","specified","file","extension"]
code: def _has_files(self, dataset_name, ext=None):
      """Determines whether some downloaded and unzipped dataset provides
      both training and test data in the given format.
  
      Parameters
      ----------
      dataset_name : str
          the name of the dataset
      ext : str or None
          the file extension without a dot, e.g `"txt"` or `"arff"`;
          if set to None (the default), `True` will be returned if either TXT
          or ARFF files are present
  
      Returns
      -------
      bool
          if there are both training and test files with the specified
          file extension
      """
      if ext is None:
          return self._has_files(
              dataset_name, ext="txt"
          ) or self._has_files(dataset_name, ext="arff")
      else:
          dataset_name = self._filenames.get(dataset_name, dataset_name)
          full_path = os.path.join(self._data_dir, dataset_name)
          basename = os.path.join(full_path, dataset_name)
          return os.path.exists(
              basename + "_TRAIN.%s" % ext
          ) and os.path.exists(basename + "_TEST.%s" % ext)
  
file_path: tslearn/tslearn/datasets/ucr_uea.py
imports: import os
  import warnings
  import csv
  import shutil
  from urllib.request import urlretrieve
  from tslearn.utils import _load_arff_uea, _load_txt_uea
  from .datasets import in_file_string_replace, extract_from_zip_url
label_desc: Use image node_id 8 for calling the UCR_UEA_datasets obj's underlying member method code with example usage: obj._has_files(dataset_name, ext) and returns: unknown, unknown