class_docstr: Aggregation method which performs binned min-max aggregation over fully
  overlapping windows.
  
  This is arguably the most computational efficient downsampling method, as it only
  performs (non-expensive) comparisons on the data in a single pass.
  
  Details on visual representativeness & stability: [https://arxiv.org/abs/2304.00900](https://arxiv.org/abs/2304.00900)
  
  !!! note
  
      This method is rather efficient when scaling to large data sizes and can be used
      as a data-reduction step before feeding it to the [`LTTB`][aggregation.aggregators.LTTB]
      algorithm, as [`MinMaxLTTB`][aggregation.aggregators.MinMaxLTTB] does with the
      [`MinMaxOverlapAggregator`][aggregation.aggregators.MinMaxOverlapAggregator].
class_docstr_tok: ["Aggregation","method","which","performs","binned","min-max","aggregation","over","fully","overlapping","windows",".","This","is","arguably","the","most","computational","efficient","downsampling","method",",","as","it","only","performs","(","non-expensive",")","comparisons","on","the","data","in","a","single","pass",".","Details","on","visual","representativeness","&","stability",":","[","https",":","\/\/arxiv.org\/abs\/2304.00900","]","(","https",":","\/\/arxiv.org\/abs\/2304.00900",")","!","!","!","note","This","method","is","rather","efficient","when","scaling","to","large","data","sizes","and","can","be","used","as","a","data-reduction","step","before","feeding","it","to","the","[","`","LTTB","`","]","[","aggregation.aggregators.LTTB","]","algorithm",",","as","[","`","MinMaxLTTB","`","]","[","aggregation.aggregators.MinMaxLTTB","]","does","with","the","[","`","MinMaxOverlapAggregator","`","]","[","aggregation.aggregators.MinMaxOverlapAggregator","]","."]
code: def _arg_downsample(
      self,
      x: np.ndarray | None,
      y: np.ndarray,
      n_out: int,
  ) -> np.ndarray:
      return self.downsampler.downsample(
          *_to_tsdownsample_args(x, y),
          n_out=n_out,
          **self.downsample_kwargs,
      )
  
imports: from __future__ import annotations
  import math
  from typing import Tuple
  import numpy
  from tsdownsample import EveryNthDownsampler, LTTBDownsampler, MinMaxDownsampler, MinMaxLTTBDownsampler
  from ..aggregation.aggregation_interface import DataAggregator, DataPointSelector
label_desc: Use image node_id 2 for calling the MinMaxAggregator obj's underlying member method code with example usage: obj._arg_downsample(x, y, n_out) and returns: self