class_docstr: Student's t-distribution (sktime native).
  
  Parameters
  ----------
  mean : float or array of float (1D or 2D)
      mean of the t-distribution distribution
  sd : float or array of float (1D or 2D), must be positive
      standard deviation of the t-distribution distribution
  df : float or array of float (1D or 2D), must be positive
      Degrees of freedom of the t-distribution
  index : pd.Index, optional, default = RangeIndex
  columns : pd.Index, optional, default = RangeIndex
  
  Example
  -------
  >>> from sktime.proba.t import TDistribution
  
  >>> n = TDistribution(mu=[[0, 1], [2, 3], [4, 5]], sigma=1, df=10)
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docstr: Return testing parameter settings for the estimator.
docstr_tok: ["Return","testing","parameter","settings","for","the","estimator","."]
code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator."""
      params1 = {"mu": [[0, 1], [2, 3], [4, 5]], "sigma": 1}
      params2 = {
          "mu": 0,
          "sigma": 1,
          "index": pd.Index([1, 2, 5]),
          "columns": pd.Index(["a", "b"]),
      }
      return [params1, params2]
  
file_path: sktime/sktime/proba/t.py
imports: import numpy
  import pandas
  from scipy.special import betaincinv, gamma, hyp2f1, loggamma
  from sktime.proba.base import BaseDistribution
label_desc: Use image node_id 8 for calling the TDistribution obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list