class_docstr: Apply a power transform sample-wise to make data more Gaussian-like.
  
  Power transforms are a family of parametric, monotonic transformations
  that are applied to make data more Gaussian-like. This is useful for
  modeling issues related to heteroscedasticity (non-constant variance),
  or other situations where normality is desired.
  
  Currently, PowerTransformer supports the Box-Cox transform and the
  Yeo-Johnson transform. The optimal parameter for stabilizing variance and
  minimizing skewness is estimated through maximum likelihood.
  
  Box-Cox requires input data to be strictly positive, while Yeo-Johnson
  supports both positive or negative data.
  
  By default, zero-mean, unit-variance normalization is applied to the
  transformed data.
  
  Parameters
  ----------
  method : 'yeo-johnson' or 'box-cox' (default = 'yeo-johnson')
      The power transform method. Available methods are:
  
      - 'yeo-johnson' [1]_, works with positive and negative values
      - 'box-cox' [2]_, only works with strictly positive values
  
  standardize : boolean (default = True)
      Set to True to apply zero-mean, unit-variance normalization to the
      transformed output.
  
  Notes
  -----
  NaNs are treated as missing values: disregarded in ``fit``, and maintained
  in ``transform``.
  
  References
  ----------
  .. [1] I.K. Yeo and R.A. Johnson, "A new family of power transformations to
         improve normality or symmetry." Biometrika, 87(4), pp.954-959,
         (2000).
  .. [2] G.E.P. Box and D.R. Cox, "An Analysis of Transformations", Journal
         of the Royal Statistical Society B, 26, 211-252 (1964).
  
  Examples
  --------
  >>> import numpy as np
  >>> from pyts.preprocessing import PowerTransformer
  >>> X = [[1, 3, 4], [2, 2, 5]]
  >>> pt = PowerTransformer()
  >>> print(pt.transform(X))
  [[-1.316...  0.209...  1.106...]
   [-0.707... -0.707...  1.414...]]
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docstr: Transform the data.
  
  Parameters
  ----------
  X : array-like, shape = (n_samples, n_timestamps)
      Data to transform.
  
  Returns
  -------
  X_new : array-like, shape = (n_samples, n_timestamps)
      Transformed data.
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code: def transform(self, X):
      """Transform the data.
  
      Parameters
      ----------
      X : array-like, shape = (n_samples, n_timestamps)
          Data to transform.
  
      Returns
      -------
      X_new : array-like, shape = (n_samples, n_timestamps)
          Transformed data.
  
      """
      X = check_array(X, dtype="float64", force_all_finite="allow-nan")
      transformer = SklearnPowerTransformer(
          method=self.method, standardize=self.standardize
      )
      X_new = transformer.fit_transform(X.T).T
      return X_new
  
file_path: pyts/pyts/preprocessing/transformer.py
imports: from sklearn.base import BaseEstimator
  from sklearn.preprocessing import PowerTransformer
  from sklearn.preprocessing import QuantileTransformer
  from sklearn.utils.validation import check_array
  from ..base import UnivariateTransformerMixin
label_desc: Use image node_id 3 for calling the PowerTransformer obj's underlying member method code with example usage: obj.transform(X) and returns: X_new