class_docstr: Time series signature kernel, including high-order and low-rank variants.
  
  Implements the signature kernel of Kiraly et al, see [1]_ and [2]_,
  including higher-order and low-rank approximation variants described therein.
  
  Parameters
  ----------
  kernel : sktime pairwise (tabular) transformer, callable, or None
      inner (tabular) kernel used in the signature sequence kernel
      if callable: function (2D np.ndarray x 2D np.ndarray) -> 2D np.ndarray
      pairwise kernel function, matrix sizes (n, d) x (m, d) -> (n x m)
      optional, default = None = Euclidean (linear) kernel with scale parameter 1
  level : int, optional, default = 2
      an integer >= 1, representing the level of truncation of the sequential kernel
  degree : int, optional, default = 1
      an integer >= 1, representing the order of approximation of sequential kernel
      can be set only if lowrank = False, otherwise ignored (always = 1)
  theta : float, optional, default=1.0
      a positive scaling factor for the levels, i-th level is scaled by theta^i
  normalize : bool, optional, default = False
      whether the output kernel matrix is normalized
      if True, sums and cumsums are divided by prod(K.shape)
  lowrank : bool, optional, default = False
      whether to use low rank approximation in computing the kernel
  rankbound : int, optional, default = infinity
      a hard threshold for the rank of the level matrices
      used only if lowrank = True
  
  References
  ----------
  .. [1] F. Kiraly, H. Oberhauser. 2016. "Kernels for sequentially ordered data.",
      arXiv: 1601.08169.
  .. [2] F. Kiraly, H. Oberhauser. 2019. "Kernels for sequentially ordered data.",
      Journal of Machine Learning Research.
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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.
      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`
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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.
          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`
      """
      param1 = {}
  
      # test higher level and normalization
      param2 = {"level": 3, "normalize": True}
  
      # test higher-order function
      param3 = {"degree": 2}
  
      # test low-rank approximation
      param4 = {"lowrank": True}
  
      paramlist = [param1, param2, param3, param4]
  
      return paramlist
  
imports: import collections
  from functools import partial
  import numpy
  from scipy.sparse.linalg import svds
  from sklearn.base import BaseEstimator, TransformerMixin
  from sktime.dists_kernels.base import BasePairwiseTransformerPanel
label_desc: Use image node_id 3 for calling the SignatureKernel obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: paramlist