class_docstr: Variable-wise aggregate of multivariate kernel or distance function.
  
  A common baseline method to turn a univariate time series distance or kernel
  into a multivariate time series distance or kernel.
  
  Also sometimes known as "independent distance" in the special case where `aggfun`
  is the sum or mean and the pairwise transformer is a time series distance.
  
  Formal details (for real valued objects, mixed typed rows in analogy):
  Let :math:`d: \mathbb{R}^n \times \mathbb{R}^n\rightarrow \mathbb{R}`
  be the pairwise function in `dist`, when applied to univariate series of length
  :math:`n`.
  This class represents the pairwise function
  :math:`d_g: \mathbb{R}^{n\times D} \times \mathbb{R}^{n\times D}\rightarrow \mathbb{R}`
  defined as :math:`d_g(x, y) := g(d(x_1, y_1), \dots, d(x_D, y_D))`,
  where :math:`x_i`, :math:`y_i` denote the :math:`i`-th column,
  and :math:`x`, `:math:`y` are interpreted as multivariate time series with
  :math:`D` variables, and where :math:`g` is a function
  :math:`g: \mathbb{R}^D\times \mathbb{R}^D \rightarrow \mathbb{R}`,
  representing the input `aggfun`.
  
  In particular, if `aggfun="sum"` (or default), then
  :math:`g(x) = \sum_{i=1}^D x_i`, and
  :math:`d_g(x, y) := \sum_{i=1}^D d(x_i, y_i)`,
  which corresponds to the usual terminology "independent distance".
  
  Parameters
  ----------
  dist : pairwise transformer of BasePairwiseTransformer scitype, or
      callable np.ndarray (n_samples, nd) x (n_samples, nd) -> (n_samples x n_samples)
  aggfun : optional, str or callable np.ndarray (m, nd, nd) -> (nd, nd)
      aggregation function over the variables, :math:`g` above
      "sum" = np.sum = default
      "mean" = np.mean
      "median" = np.median
      "max" = np.max
      "min" = np.min
      when starting with a function (m) -> scalar, use np.apply_along_axis
      to create a function (m, nd, nd) -> (nd, nd) and pass that as `aggfun`
  
  Examples
  --------
  >>> from sktime.dists_kernels.indep import IndepDist
  >>> from sktime.dists_kernels.dtw import DtwDist
  >>>
  >>> dist = IndepDist(DtwDist())
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
  
      params1 = {"dist": DtwDist()}
      params2 = {"dist": DtwDist(), "aggfun": "median"}
      params3 = {"dist": DtwDist(), "aggfun": _testfun}
  
      return [params1, params2, params3]
  
imports: import numpy
  from sktime.dists_kernels.base import BasePairwiseTransformerPanel
label_desc: Use image node_id 3 for calling the IndepDist obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list