class_docstr: Piecewise Aggregate Approximation (PAA) transformation.
  
  PAA was originally presented in [1]_.
  
  Parameters
  ----------
  n_segments : int (default: 1)
      Number of PAA segments to compute
  
  Notes
  -----
      This method requires a dataset of equal-sized time series.
  
  Examples
  --------
  >>> paa = PiecewiseAggregateApproximation(n_segments=3)
  >>> data = [[-1., 2., 0.1, -1., 1., -1.], [1., 3.2, -1., -3., 1., -1.]]
  >>> paa_data = paa.fit_transform(data)
  >>> paa_data.shape
  (2, 3, 1)
  >>> paa_data
  array([[[ 0.5 ],
          [-0.45],
          [ 0.  ]],
  <BLANKLINE>
         [[ 2.1 ],
          [-2.  ],
          [ 0.  ]]])
  >>> paa.distance_paa(paa_data[0], paa_data[1])  # doctest: +ELLIPSIS
  3.15039...
  >>> paa.distance(data[0], data[1])  # doctest: +ELLIPSIS
  3.15039...
  >>> paa.inverse_transform(paa_data)
  array([[[ 0.5 ],
          [ 0.5 ],
          [-0.45],
          [-0.45],
          [ 0.  ],
          [ 0.  ]],
  <BLANKLINE>
         [[ 2.1 ],
          [ 2.1 ],
          [-2.  ],
          [-2.  ],
          [ 0.  ],
          [ 0.  ]]])
  
  References
  ----------
  .. [1] E. Keogh & M. Pazzani. Scaling up dynamic time warping for
     datamining applications. SIGKDD 2000, pp. 285--289.
docstr: Compute time series corresponding to given PAA representations.
  
  Parameters
  ----------
  X : array-like of shape (n_ts, sz_paa, d)
      A dataset of PAA series.
  
  Returns
  -------
  numpy.ndarray of shape (n_ts, sz_original_ts, d)
      A dataset of time series corresponding to the provided
      representation.
docstr_tok: ["Compute","time","series","corresponding","to","given","PAA","representations",".","Parameters","--","--","--","--","--","X",":","array-like","of","shape","(","n_ts",",","sz_paa",",","d",")","A","dataset","of","PAA","series",".","Returns","--","--","--","-","numpy.ndarray","of","shape","(","n_ts",",","sz_original_ts",",","d",")","A","dataset","of","time","series","corresponding","to","the","provided","representation","."]
code: def inverse_transform(self, X):
      """Compute time series corresponding to given PAA representations.
  
      Parameters
      ----------
      X : array-like of shape (n_ts, sz_paa, d)
          A dataset of PAA series.
  
      Returns
      -------
      numpy.ndarray of shape (n_ts, sz_original_ts, d)
          A dataset of time series corresponding to the provided
          representation.
      """
      self._is_fitted()
      X = check_array(X, allow_nd=True, force_all_finite=False)
      X = check_dims(X)
      return inv_transform_paa(X, original_size=self._X_fit_dims_[1])
  
imports: import numpy
  from scipy.stats import norm
  from sklearn.base import TransformerMixin
  from sklearn.utils.validation import check_is_fitted, check_array
  from tslearn.bases import BaseModelPackage, TimeSeriesBaseEstimator
  from tslearn.metrics.cysax import cydist_sax, cyslopes, cydist_1d_sax, inv_transform_1d_sax, inv_transform_sax, inv_transform_paa
  from tslearn.utils import ts_size, check_dims
label_desc: Use image node_id 10 for calling the PiecewiseAggregateApproximation obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: inv_transform_paa