class_docstr: Efficient version off LTTB by first reducing really large datasets with
  the [`MinMaxAggregator`][aggregation.aggregators.MinMaxAggregator] and then further aggregating the
  reduced result with [`LTTB`][aggregation.aggregators.LTTB].
  
  Starting from 10M data points, this method performs the MinMax-prefetching of data
  points to enhance computational efficiency.
  
  Inventors: Jonas & Jeroen Van Der Donckt - 2022
  
  Paper: [https://arxiv.org/pdf/2305.00332.pdf](https://arxiv.org/pdf/2305.00332.pdf)
class_docstr_tok: ["Efficient","version","off","LTTB","by","first","reducing","really","large","datasets","with","the","[","`","MinMaxAggregator","`","]","[","aggregation.aggregators.MinMaxAggregator","]","and","then","further","aggregating","the","reduced","result","with","[","`","LTTB","`","]","[","aggregation.aggregators.LTTB","]",".","Starting","from","10M","data","points",",","this","method","performs","the","MinMax-prefetching","of","data","points","to","enhance","computational","efficiency",".","Inventors",":","Jonas","&","Jeroen","Van","Der","Donckt","-","2022","Paper",":","[","https",":","\/\/arxiv.org\/pdf\/2305.00332.pdf","]","(","https",":","\/\/arxiv.org\/pdf\/2305.00332.pdf",")"]
code: def _arg_downsample(
      self,
      x: np.ndarray | None,
      y: np.ndarray,
      n_out: int,
  ) -> np.ndarray:
      return self.minmaxlttb.downsample(
          *_to_tsdownsample_args(x, y),
          n_out=n_out,
          minmax_ratio=self.minmax_ratio,
          **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 MinMaxLTTB obj's underlying member method code with example usage: obj._arg_downsample(x, y, n_out) and returns: self