class_docstr: Base adapter mixin for tslearn distances and kernels.
docstr: Evaluate tslearn pwtrafo on two time series.
  
  The default returns of _get_tslearn_pwtrafo
  evaluated at X1, X2 and self.get_params
  
  Parameters
  ----------
  X, X2: 2D np.ndarrays of format (n_variables, n_timepoints)
      two time series to compute the pairwise transform on
  
  Returns
  -------
  float
      _get_tslearn_pwtrafo result evaluated at X1, X2, and self.get_params()
docstr_tok: ["Evaluate","tslearn","pwtrafo","on","two","time","series",".","The","default","returns","of","_get_tslearn_pwtrafo","evaluated","at","X1",",","X2","and","self.get_params","Parameters","--","--","--","--","--","X",",","X2",":","2D","np.ndarrays","of","format","(","n_variables",",","n_timepoints",")","two","time","series","to","compute","the","pairwise","transform","on","Returns","--","--","--","-","float","_get_tslearn_pwtrafo","result","evaluated","at","X1",",","X2",",","and","self.get_params","(",")"]
code: def _eval_tslearn_pwtrafo(self, X, X2=None):
      """Evaluate tslearn pwtrafo on two time series.
  
      The default returns of _get_tslearn_pwtrafo
      evaluated at X1, X2 and self.get_params
  
      Parameters
      ----------
      X, X2: 2D np.ndarrays of format (n_variables, n_timepoints)
          two time series to compute the pairwise transform on
  
      Returns
      -------
      float
          _get_tslearn_pwtrafo result evaluated at X1, X2, and self.get_params()
      """
      if X2 is None:
          X2 = X
  
      pwtrafo = self._get_tslearn_pwtrafo()
      params = self.get_params()
      if self._inner_params is not None:
          params = _subset_dict(params, self._inner_params)
  
      return pwtrafo(X, X2, **params)
  
imports: import numpy
label_desc: Use image node_id 2 for calling the _TslearnPwTrafoAdapter obj's underlying member method code with example usage: obj._eval_tslearn_pwtrafo(X, X2) and returns: pwtrafo