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 get_boundaries(self) -> np.ndarray:
      return (
          self.thresholds.astype(int).clip(min=0, max=self.domain - 1)
          + 0.5
      )
  
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 7 for calling the Staircase obj's underlying member method code with example usage: obj.get_boundaries() and returns: unknown