code: def _paa(X, n_samples, n_timestamps, start, end, n_timestamps_new):
      X_paa = np.empty((n_samples, n_timestamps_new))
      for i in prange(n_samples):
          for j in prange(n_timestamps_new):
              X_paa[i, j] = np.mean(X[i, start[j] : end[j]])
      return X_paa
  
file_path: pyts/pyts/approximation/paa.py
imports: import numpy
  from math import ceil
  from numba import njit, prange
  from sklearn.base import BaseEstimator
  from sklearn.utils.validation import check_array
  from ..base import UnivariateTransformerMixin
  from ..utils import segmentation
label_desc: Use image node_id 1 for calling a global function with example usage: _paa(X, n_samples, n_timestamps, start, end, n_timestamps_new) and returns: X_paa