code: def _load_ucr_dataset(dataset, path):
      """Load a UCR 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
      -----
      Padded values are represented as NaN's.
  
      """
      new_path = os.path.join(path, dataset)
      try:
          with open(
              os.path.join(new_path, f"{dataset}.txt"), encoding="utf-8"
          ) as f:
              description = f.read()
      except UnicodeDecodeError:
          with open(
              os.path.join(new_path, f"{dataset}.txt"),
              encoding="ISO-8859-1",
          ) as f:
              description = f.read()
      try:
          data_train = np.genfromtxt(
              os.path.join(new_path, f"{dataset}_TRAIN.txt")
          )
          data_test = np.genfromtxt(
              os.path.join(new_path, f"{dataset}_TEST.txt")
          )
  
          X_train, y_train = data_train[:, 1:], data_train[:, 0]
          X_test, y_test = data_test[:, 1:], data_test[:, 0]
  
      except IndexError:
          train = loadarff(
              os.path.join(new_path, f"{dataset}_TRAIN.txt")
          )
          test = loadarff(os.path.join(new_path, f"{dataset}_TEST.txt"))
  
          data_train = np.asarray(
              [train[0][name] for name in train[1].names()]
          )
          X_train = data_train[:-1].T.astype("float64")
          y_train = data_train[-1]
  
          data_test = np.asarray(
              [test[0][name] for name in test[1].names()]
          )
          X_test = data_test[:-1].T.astype("float64")
          y_test = data_test[-1]
  
      try:
          y_train = y_train.astype("float64").astype("int64")
          y_test = y_test.astype("float64").astype("int64")
      except ValueError:
          y_train = y_train.astype(str)
          y_test = y_test.astype(str)
  
      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/ucr.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 6 for calling a global function with example usage: _load_ucr_dataset(dataset, path) and returns: bunch