class_docstr: Interface to scipy distances.
  
  computes pairwise distances using scipy.spatial.distance.cdist
      includes Euclidean distance and p-norm (Minkowski) distance
          note: weighted distances are not supported
  
  Parameters
  ----------
  metric : string or function, as in cdist; default = 'euclidean'
      if string, one of: 'braycurtis', 'canberra', 'chebyshev', 'cityblock',
          'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard',
          'jensenshannon',
          'kulsinski' (< scipy 1.11) or 'kulczynski1' (from scipy 1.11),
          'mahalanobis', 'matching', 'minkowski',
          'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener',
          'sokalsneath', 'sqeuclidean', 'yule'
      if function, should have signature 1D-np.array x 1D-np.array -> float
  p:  if metric='minkowski', the "p" in "p-norm", otherwise irrelevant
  colalign : string, one of 'intersect' (default), 'force-align', 'none'
      controls column alignment if X, X2 passed in fit are pd.DataFrame
      columns between X and X2 are aligned via column names
      if 'intersect', distance is computed on columns occurring both in X and X2,
          other columns are discarded; column ordering in X2 is copied from X
      if 'force-align', raises an error if the set of columns in X, X2 differs;
          column ordering in X2 is copied from X
      if 'none', X and X2 are passed through unmodified (no columns are aligned)
          note: this will potentially align "non-matching" columns
  var_weights : 1D np.array of float or None, default=None
      weight/scaling vector applied to variables in X/X2
      before being passed to cdist, i-th col of X/X2 is multiplied by var_weights[i]
      if None, equivalent to all-ones vector
  metric_kwargs : dict, optional, default=None
      any kwargs passed to the metric in addition, i.e., to the function cdist
      common kwargs: "w" : array-like, same length as X.columns, weights for metric
      refer to scipy.spatial.distance.dist for a documentation of other extra kwargs
docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
      There are currently no reserved values for distance/kernel transformers.
  
  Returns
  -------
  params : dict or list of dict, default = {}
      Parameters to create testing instances of the class
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`
docstr_tok: ["Return","testing","parameter","settings","for","the","estimator",".","Parameters","--","--","--","--","--","parameter_set",":","str",",","default=","''","default","''","Name","of","the","set","of","test","parameters","to","return",",","for","use","in","tests",".","If","no","special","parameters","are","defined","for","a","value",",","will","return","`","``","default","''","`","set",".","There","are","currently","no","reserved","values","for","distance\/kernel","transformers",".","Returns","--","--","--","-","params",":","dict","or","list","of","dict",",","default","=","{","}","Parameters","to","create","testing","instances","of","the","class","Each","dict","are","parameters","to","construct","an","``","interesting","''","test","instance",",","i.e.",",","`","MyClass","(","*","*","params",")","`","or","`","MyClass","(","*","*","params","[","i","]",")","`","creates","a","valid","test","instance",".","`","create_test_instance","`","uses","the","first","(","or","only",")","dictionary","in","`","params","`"]
code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
          There are currently no reserved values for distance/kernel transformers.
  
      Returns
      -------
      params : dict or list of dict, default = {}
          Parameters to create testing instances of the class
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`
      """
      # default settings
      params1 = {}
  
      # using kwargs
      params2 = {"metric": "minkowski", "p": 3}
  
      return [params1, params2]
  
file_path: sktime/sktime/dists_kernels/scipy_dist.py
imports: import numpy
  import pandas
  from scipy.spatial.distance import cdist
  from sktime.dists_kernels.base import BasePairwiseTransformer
label_desc: Use image node_id 3 for calling the ScipyDist obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list