class_docstr: Aggregates Panel data containing overlapping windows of one time series.
  
  The input data contains multiple overlapping time series elements that could
  arranged as follows:
  xxxx.....
  .xxxx....
  ..xxxx...
  ...xxxx..
  ....xxxx.
  .....xxxx
  ......xxxx
  .......xxxx
  ........xxxx
  .........xxxx
  The merger aggregates the data by aligning the time series windows as shown above
  and applying a aggregation function to the overlapping data points.
  The aggregation function can be one of "mean" or "median". I.e., the `mean` or
  `median` of each column is calculated, resulting in a univariate time series.
  
  Parameters
  ----------
  method : {`median`, `mean`}, default="median"
      The method to use for aggregation. Can be one of "mean" or "median".
  stride : int, default=0
      The stride to use for the aggregation. The stride determines the number of
      shifts between consecutive instances. A stride of 0 means no shift. A
      stride of 1 means that the time series is aggregated as above.
  
  Examples
  --------
  >>> from sktime.transformations.merger import Merger
  >>> from sktime.utils._testing.panel import _make_panel
  >>> y = _make_panel(n_instances=10, n_columns=3, n_timepoints=5)
  >>> result = Merger(method="median").fit_transform(y)
  >>> result.shape
  (3, 5)
  
  >>> from sktime.transformations.merger import Merger
  >>> from sktime.utils._testing.panel import _make_panel
  >>> y = _make_panel(n_instances=10, n_columns=3, n_timepoints=5)
  >>> result = Merger(method="median", stride=1).fit_transform(y)
  >>> result.shape
  (14, 3)
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 forecasters.
  
  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 forecasters.
  
      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`
      """
      return [{"method": "mean"}, {"method": "median"}]
  
imports: import numpy
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 4 for calling the Merger obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list