class_docstr: Scaler for time series. Scales time series so that their mean (resp.
  standard deviation) in each dimension is
  mu (resp. std).
  
  Parameters
  ----------
  mu : float (default: 0.)
      Mean of the output time series.
  std : float (default: 1.)
      Standard deviation of the output time series.
  
  Notes
  -----
      This method requires a dataset of equal-sized time series.
  
      NaNs within a time series are ignored when calculating mu and std.
  
  Examples
  --------
  >>> TimeSeriesScalerMeanVariance(mu=0.,
  ...                              std=1.).fit_transform([[0, 3, 6]])
  array([[[-1.22474487],
          [ 0.        ],
          [ 1.22474487]]])
  >>> TimeSeriesScalerMeanVariance(mu=0.,
  ...                              std=1.).fit_transform([[numpy.nan, 3, 6]])
  array([[[nan],
          [-1.],
          [ 1.]]])
class_docstr_tok: ["Scaler","for","time","series",".","Scales","time","series","so","that","their","mean","(","resp",".","standard","deviation",")","in","each","dimension","is","mu","(","resp",".","std",")",".","Parameters","--","--","--","--","--","mu",":","float","(","default",":","0",".",")","Mean","of","the","output","time","series",".","std",":","float","(","default",":","1",".",")","Standard","deviation","of","the","output","time","series",".","Notes","--","--","-","This","method","requires","a","dataset","of","equal-sized","time","series",".","NaNs","within","a","time","series","are","ignored","when","calculating","mu","and","std",".","Examples","--","--","--","--",">",">",">","TimeSeriesScalerMeanVariance","(","mu=0.",",","...","std=1",".",")",".fit_transform","(","[","[","0",",","3",",","6","]","]",")","array","(","[","[","[","-1.22474487","]",",","[","0",".","]",",","[","1.22474487","]","]","]",")",">",">",">","TimeSeriesScalerMeanVariance","(","mu=0.",",","...","std=1",".",")",".fit_transform","(","[","[","numpy.nan",",","3",",","6","]","]",")","array","(","[","[","[","nan","]",",","[","-1",".","]",",","[","1",".","]","]","]",")"]
docstr: A dummy method such that it complies to the sklearn requirements.
  Since this method is completely stateless, it just returns itself.
  
  Parameters
  ----------
  X
      Ignored
  
  Returns
  -------
  self
docstr_tok: ["A","dummy","method","such","that","it","complies","to","the","sklearn","requirements",".","Since","this","method","is","completely","stateless",",","it","just","returns","itself",".","Parameters","--","--","--","--","--","X","Ignored","Returns","--","--","--","-","self"]
code: def fit(self, X, y=None, **kwargs):
      """A dummy method such that it complies to the sklearn requirements.
      Since this method is completely stateless, it just returns itself.
  
      Parameters
      ----------
      X
          Ignored
  
      Returns
      -------
      self
      """
      X = check_array(X, allow_nd=True, force_all_finite=False)
      X = to_time_series_dataset(X)
      self._X_fit_dims = X.shape
      return self
  
imports: import numpy
  from sklearn.base import TransformerMixin
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from scipy.interpolate import interp1d
  import warnings
  from tslearn.utils import to_time_series_dataset, check_equal_size, ts_size, check_dims
  from tslearn.bases import TimeSeriesBaseEstimator
label_desc: Use image node_id 2 for calling the TimeSeriesScalerMeanVariance obj's underlying member method code with example usage: obj.fit(X, y) and returns: self