class_docstr: A class for learning a staircase decision tree.
  
  Args:
      domain: The number of points in the domain.
      n_classes: The number of classes.
class_docstr_tok: ["A","class","for","learning","a","staircase","decision","tree",".","Args",":","domain",":","The","number","of","points","in","the","domain",".","n_classes",":","The","number","of","classes","."]
code: def __init__(self, domain: int, n_classes: int) -> None:
      self.domain = domain
      self.classes = n_classes
      assert domain > 0
      assert n_classes > 0
      self.thresholds = (
          self._back_thres
      ) = self._forward_thres = np.linspace(
          domain / n_classes, domain, n_classes - 1, endpoint=False
      )
      self.uncertainty = np.zeros_like(self.thresholds)
  
imports: from collections import deque
  import operator
  import itertools
  from typing import Optional, List, Tuple
  import numpy
  import warnings
label_desc: Use image node_id 1 to create a new Staircase object  with example: obj = Staircase(domain, n_classes)