code: def load_from_ucr_tsv_to_dataframe(
      full_file_path_and_name, return_separate_X_and_y=True
  ):
      """Load data from a .tsv file into a Pandas DataFrame.
  
      Parameters
      ----------
      full_file_path_and_name: str
          The full pathname of the .tsv file to read.
      return_separate_X_and_y: bool
          true then X and Y values should be returned as separate Data Frames (
          X) and a numpy array (y), false otherwise.
          This is only relevant for data.
  
      Returns
      -------
      DataFrame, ndarray
          If return_separate_X_and_y then a tuple containing a DataFrame and a
          numpy array containing the relevant time-series and corresponding
          class values.
      DataFrame
          If not return_separate_X_and_y then a single DataFrame containing
          all time-series and (if relevant) a column "class_vals" the
          associated class values.
      """
      df = pd.read_csv(full_file_path_and_name, sep="\t", header=None)
      y = df.pop(0).values
      df.columns -= 1
      X = pd.DataFrame()
      X["dim_0"] = [pd.Series(df.iloc[x, :]) for x in range(len(df))]
      if return_separate_X_and_y is True:
          return X, y
      X["class_val"] = y
      return X
  
label_desc: Use image node_id 1 for calling a global function with example usage: load_from_ucr_tsv_to_dataframe(full_file_path_and_name, return_separate_X_and_y) and returns: X, X,  y