code: def inv_transform_1d_sax(
      dataset_sax,
      breakpoints_avg_middle_,
      breakpoints_slope_middle_,
      original_size,
  ):
      """Compute time series corresponding to given 1d-SAX representations.
  
      Parameters
      ----------
      dataset_sax : array-like, shape=(n_ts, sz, 2 * d), dtype=float64 (Linux and MacOS) or float32 (Windows)
          A dataset of SAX series.
      breakpoints_avg_middle_ : array-like, ndim=1, dtype=float64
      breakpoints_slope_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
          A dataset of time series corresponding to the provided
              representation.
      """
      n_ts, sz, d_1d_sax = dataset_sax.shape
      d = d_1d_sax // 2
      seg_sz = original_size // sz
      dataset_out = np.empty((n_ts, original_size, d))
  
      for i in prange(n_ts):
          for t in range(sz):
              t0 = t * seg_sz
              t_middle = float(t0) + 0.5 * (seg_sz - 1)
              for di in range(d):
                  avg = breakpoints_avg_middle_[dataset_sax[i, t, di]]
                  slope = breakpoints_slope_middle_[
                      dataset_sax[i, t, di + d]
                  ]
                  for tt in range(t0, seg_sz * (t + 1)):
                      dataset_out[i, tt, di] = avg + slope * (
                          tt - t_middle
                      )
      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 6 for calling a global function with example usage: inv_transform_1d_sax(dataset_sax, breakpoints_avg_middle_, breakpoints_slope_middle_, original_size) and returns: dataset_out