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)
code: def _align_temporal(self, horizon, x):
      x = x.astype(float)
      if self.stride > 0:
          x = np.insert(
              x,
              np.arange(1, x.shape[0]).repeat(self.stride - 1),
              np.nan,
              axis=0,
          )
      elif self.stride == 0:
          return x
      r = []
      for i in range(horizon):
          _res = np.concatenate(
              [
                  np.full(fill_value=np.nan, shape=(i, x.shape[1])),
                  x[:, :, i],
                  np.full(
                      fill_value=np.nan,
                      shape=((horizon - 1 - i), x.shape[1]),
                  ),
              ]
          )
          r.append(_res)
      return np.stack(r)
  
imports: import numpy
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 3 for calling the Merger obj's underlying member method code with example usage: obj._align_temporal(horizon, x) and returns: np, x