code: def inv_transform_paa(dataset_paa, original_size):
      """Compute time series corresponding to given PAA representations.
  
      Parameters
      ----------
      dataset_paa : array-like, shape=(n_ts, sz, d), dtype=float64
          A dataset of PAA series.
      original_size : int32
          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 = dataset_paa.shape
      seg_sz = original_size // sz
      dataset_out = np.zeros((n_ts, original_size, d))
      for t in prange(sz):
          t0 = t * seg_sz
          for i_ts in range(n_ts):
              dataset_out[i_ts, t0 : t0 + seg_sz, :] = dataset_paa[
                  i_ts, 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 1 for calling a global function with example usage: inv_transform_paa(dataset_paa, original_size) and returns: dataset_out