class_docstr: Soft dynamic time warping distance, from tslearn.
  
  Direct interface to ``tslearn.metrics.cdist_soft_dtw`` and
  ``tslearn.metrics.cdist_soft_dtw_normalized``.
  
  Parameters
  ----------
  normalized : bool, default = False
      Whether the DTW distance should be normalized.
      If ``False``, interfaces ``tslearn.metrics.cdist_soft_dtw``.
      If ``True``, interfaces ``tslearn.metrics.cdist_soft_dtw_normalized``.
  gamma : float (default 1.)
      Gamma parameter for Soft-DTW
  
  References
  ----------
  .. [1] M. Cuturi, M. Blondel "Soft-DTW: a Differentiable Loss Function for
     Time-Series," ICML 2017.
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docstr: Adapter method to get tslearn pwtrafo.
docstr_tok: ["Adapter","method","to","get","tslearn","pwtrafo","."]
code: def _get_tslearn_pwtrafo(self):
      """Adapter method to get tslearn pwtrafo."""
      from tslearn.metrics.softdtw_variants import (
          cdist_soft_dtw,
          cdist_soft_dtw_normalized,
      )
  
      if self.normalized:
          return cdist_soft_dtw_normalized
      else:
          return cdist_soft_dtw
  
file_path: sktime/sktime/dists_kernels/dtw/_dtw_tslearn.py
imports: from sktime.dists_kernels.base import BasePairwiseTransformerPanel
  from sktime.dists_kernels.base.adapters import _TslearnPwTrafoAdapter
label_desc: Use image node_id 2 for calling the SoftDtwDistTslearn obj's underlying member method code with example usage: obj._get_tslearn_pwtrafo() and returns: cdist_soft_dtw_normalized, cdist_soft_dtw