class_docstr: Plateau finder transformer.
  
  Transformer that finds segments of the same given value, plateau in
  the time series, and
  returns the starting indices and lengths.
  
  Parameters
  ----------
  value : {int, float, np.nan, np.inf}
      Value for which to find segments
  min_length : int
      Minimum lengths of segments with same value to include.
      If min_length is set to 1, the transformer can be used as a value
      finder.
class_docstr_tok: ["Plateau","finder","transformer",".","Transformer","that","finds","segments","of","the","same","given","value",",","plateau","in","the","time","series",",","and","returns","the","starting","indices","and","lengths",".","Parameters","--","--","--","--","--","value",":","{","int",",","float",",","np.nan",",","np.inf","}","Value","for","which","to","find","segments","min_length",":","int","Minimum","lengths","of","segments","with","same","value","to","include",".","If","min_length","is","set","to","1",",","the","transformer","can","be","used","as","a","value","finder","."]
code: def __init__(self, value=np.nan, min_length=2):
      self.value = value
      self.min_length = min_length
      super().__init__()
  
imports: import numpy
  import pandas
  from joblib import Parallel, delayed
  from sktime.datatypes import convert_to
  from sktime.transformations.base import BaseTransformer
  from sktime.transformations.panel.segment import RandomIntervalSegmenter
  from sktime.utils.pandas import df_map
label_desc: Use image node_id 1 to create a new PlateauFinder object from inherited base classes: BaseTransformer with example: obj = PlateauFinder(value, min_length)