class_docstr: A simple class to model a Shapelet with associated information.
  
  Parameters
  ----------
  series_id: int
      The index of the series within the data (X) that was passed to fit.
  start_pos: int
      The starting position of the shapelet within the original series
  length: int
      The length of the shapelet
  info_gain: float
      The calculated information gain of this shapelet
  data: array-like
      The (z-normalised) data of this shapelet.
docstr: Print.
docstr_tok: ["Print","."]
code: def __str__(self):
      """Print."""
      return (
          "Series ID: {}, start_pos: {}, length: {}, info_gain: {},"
          " ".format(
              self.series_id,
              self.start_pos,
              self.length,
              self.info_gain,
          )
      )
  
imports: import heapq
  import time
  import warnings
  from itertools import zip_longest
  from operator import itemgetter
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from sklearn import preprocessing
  from sklearn.utils import check_random_state
  from sklearn.utils.multiclass import class_distribution
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.validation import check_n_jobs
label_desc: Use image node_id 2 for calling the Shapelet obj's underlying member method code with example usage: obj.__str__() and returns: str