code: def load_time_series_txt(fname):
      """Loads a time series dataset from disk.
  
      Parameters
      ----------
      fname : string
          Path to the file from which time series should be read.
  
      Returns
      -------
      numpy.ndarray or array of numpy.ndarray
          The dataset of time series.
  
      Examples
      --------
      >>> dataset = to_time_series_dataset([[1, 2, 3, 4], [1, 2, 3]])
      >>> save_time_series_txt("tmp-tslearn-test.txt", dataset)
      >>> reloaded_dataset = load_time_series_txt("tmp-tslearn-test.txt")
  
      See Also
      --------
      save_time_series_txt : Save time series to disk
      """
      with open(fname, "r") as f:
          return to_time_series_dataset(
              [str_to_time_series(row) for row in f.readlines()]
          )
  
imports: import warnings
  from io import StringIO
  import numpy
  from sklearn.base import TransformerMixin
  from sklearn.utils import column_or_1d
  from sklearn.utils.validation import check_is_fitted
  from tslearn.backend import instantiate_backend
  from tslearn.bases import TimeSeriesBaseEstimator
label_desc: Use image node_id 7 for calling a global function with example usage: load_time_series_txt(fname) and returns: to_time_series_dataset