code: def _load_uea_dataset(dataset, path):
      """Load a UEA data set from a local folder.
  
      Parameters
      ----------
      dataset : str
          Name of the dataset.
  
      path : str
          The path of the folder containing the cached data set.
  
      Returns
      -------
      data : Bunch
          Dictionary-like object, with attributes:
  
          data_train : array of floats
              The time series in the training set.
          data_test : array of floats
              The time series in the test set.
          target_train : array
              The classification labels in the training set.
          target_test : array
              The classification labels in the test set.
          DESCR : str
              The full description of the dataset.
          url : str
              The url of the dataset.
  
      Notes
      -----
      Missing values are represented as NaN's.
  
      """
      new_path = os.path.join(path, dataset)
      try:
          description_file = [
              file
              for file in os.listdir(new_path)
              if ("Description.txt" in file or f"{dataset}.txt" in file)
          ][0]
      except IndexError:
          description_file = None
  
      if description_file is not None:
          try:
              with open(
                  os.path.join(new_path, description_file),
                  encoding="utf-8",
              ) as f:
                  description = f.read()
          except UnicodeDecodeError:
              with open(
                  os.path.join(new_path, description_file),
                  encoding="ISO-8859-1",
              ) as f:
                  description = f.read()
      else:
          description = None
  
      data_train = loadarff(
          os.path.join(new_path, f"{dataset}_TRAIN.arff")
      )
      X_train, y_train = _parse_relational_arff(data_train)
  
      data_test = loadarff(
          os.path.join(new_path, f"{dataset}_TEST.arff")
      )
      X_test, y_test = _parse_relational_arff(data_test)
  
      bunch = Bunch(
          data_train=X_train,
          target_train=y_train,
          data_test=X_test,
          target_test=y_test,
          DESCR=description,
          url=(
              "http://www.timeseriesclassification.com/"
              "description.php?Dataset={}".format(dataset)
          ),
      )
  
      return bunch
  
file_path: pyts/pyts/datasets/uea.py
imports: import numpy
  import os
  import pickle
  from scipy.io.arff import loadarff
  from sklearn.utils import Bunch
  from urllib.request import urlretrieve
  import zipfile
label_desc: Use image node_id 5 for calling a global function with example usage: _load_uea_dataset(dataset, path) and returns: bunch