class_docstr: Kernel function obtained from a distance function.
  
  Formal details (for real valued objects, mixed typed rows in analogy):
  Let :math:`d: \mathbb{R}^D \times \mathbb{R}^D\rightarrow \mathbb{R}`
  be the pairwise function in `dist`, when applied to `D`-vectors.
  If `dist_diag=None`, then `KernelFromDist(dist)` corresponds to the kernel function
  :math:`k(x, y) := d(x, x)^2 + d(y, y)^2 - 0.5 \cdot d(x, y)^2`.
  If `dist_diag` is provided,
  and corresponds to a function :math:`f:\mathbb{R}^D \rightarrow \mathbb{R}`,
  then `KernelFromDist(dist)` corresponds to the kernel function
  :math:`k(x, y) := f(x, x)^2 + f(y, y)^2 - 0.5 \cdot d(x, y)^2`.
  
  It should be noted that :math:`k` is, in general, not positive semi-definite.
  
  Parameters
  ----------
  dist : pairwise transformer of BasePairwiseTransformer scitype, or
      callable np.ndarray (n_samples, nd) x (n_samples, nd) -> (n_samples x n_samples)
  dist_diag : pairwise transformer of BasePairwiseTransformer scitype, or
      series-to-panel transformer of Basetransformer scitype, or
      callable np.ndarray (n_samples, nd) -> (n_samples, )
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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.
  
  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`.
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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.
  
      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`.
      """
      from sktime.dists_kernels.dtw import DtwDist
      from sktime.transformations.series.adapt import (
          PandasTransformAdaptor,
      )
      from sktime.transformations.series.summarize import (
          SummaryTransformer,
      )
  
      params1 = {"dist": DtwDist()}
      t = SummaryTransformer("mean", None)
      # we need this since multivariate summary produces two columns
      # if one column, has no effect; if multiple, takes means by row
      t = PandasTransformAdaptor("mean", {"axis": 1}) * t
      params2 = {"dist": DtwDist(), "dist_diag": t}
  
      return [params1, params2]
  
file_path: sktime/sktime/dists_kernels/dist_to_kern.py
imports: import numpy
  from sktime.dists_kernels.base import BasePairwiseTransformerPanel
label_desc: Use image node_id 3 for calling the KernelFromDist obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list