code: def inv_transform_sax(
      dataset_sax, breakpoints_middle_, original_size
  ):
      """Compute time series corresponding to given SAX representations.
  
      Parameters
      ----------
      dataset_sax : array-like, shape=(n_ts, sz, d), dtype=float64 (Linux and MacOS) or float32 (Windows)
          A dataset of SAX series.
      breakpoints_middle_ : array-like, ndim=1, dtype=float64
      original_size : int64 (Linux and MacOS) or int32 (Windows)
          Length of the original time series.
  
      Returns
      -------
      dataset_out : array-like, shape=(n_ts, original_size, d), dtype=float64
      """
      n_ts, sz, d = dataset_sax.shape
      seg_sz = original_size // sz
      dataset_out = np.zeros((n_ts, original_size, d))
  
      for i in prange(n_ts):
          for t in range(sz):
              t0 = t * seg_sz
              dataset_out[i, t0 : t0 + seg_sz, :] = breakpoints_middle_[
                  dataset_sax[i, t, :]
              ]
      return dataset_out
  
file_path: tslearn/tslearn/metrics/cysax.py
imports: import numpy
  from numba import njit, objmode, prange
  from sklearn.linear_model import LinearRegression
label_desc: Use image node_id 3 for calling a global function with example usage: inv_transform_sax(dataset_sax, breakpoints_middle_, original_size) and returns: dataset_out