class_docstr: Drop missing values transformation.
  
  Drops rows or columns with missing values from X. Mostly wraps
  pandas.DataFrame.dropna, but allows specifying thresh as a fraction of
  non-missing observations.
  
  Parameters
  ----------
  axis : {0 or 'index', 1 or 'columns'}, default 0
      Determine if rows or columns which contain missing values are removed.
      Must be 0 or 'index' for univariate input.
  
      * 0, or 'index' : Drop rows which contain missing values.
      * 1, or 'columns' : Drop columns which contain missing value.
  
  how : {'any', 'all'}, default 'any'
      Determine if row or column is removed from DataFrame, when we have
      at least one NA or all NA.
  
      * 'any' : If any NA values are present, drop that row or column.
      * 'all' : If all values are NA, drop that row or column.
  
  thresh : int or float, optional
       If int, require at least that many non-NA values (as in pandas.dropna).
       If float, minimum share of non-NA values for rows/columns to be
       retained. Fraction must be contained within (0,1]. Setting fraction
       to 1.0 is equivalent to setting how='any'. thresh cannot be combined
       with how.
  
  remember : bool, default False if axis==0, True if axis==1
      If True, drops the same rows/columns in transform as in fit. If false,
      drops rows/columns according to the NAs seen in transform (equivalent
      to PandasTransformAdaptor(method="dropna")).
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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 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 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`
      """
      params = [
          {"axis": 0, "how": "any", "thresh": None},
          {"axis": 1, "how": "any", "thresh": None},
          {"axis": 0, "how": "all", "thresh": None},
          {"axis": 1, "how": "all", "thresh": None},
          {"axis": 0, "how": None, "thresh": 0.9},
          {"axis": 1, "how": None, "thresh": 0.9},
          {"axis": 1, "how": None, "thresh": 3},
      ]
  
      return params
  
file_path: sktime/sktime/transformations/series/dropna.py
imports: from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 8 for calling the DropNA obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params