class_docstr: Class containing a convenient in-memory representation of a decision tree.
class_docstr_tok: ["Class","containing","a","convenient","in-memory","representation","of","a","decision","tree","."]
docstr: Args:
      lefts: The id of the left nodes
      rights: The id of the right nodes
      feature: The features used to make decisions
      thresholds: The thresholds used in the decisions
      values: The value stored in the leaves
docstr_tok: ["Args",":","lefts",":","The","id","of","the","left","nodes","rights",":","The","id","of","the","right","nodes","feature",":","The","features","used","to","make","decisions","thresholds",":","The","thresholds","used","in","the","decisions","values",":","The","value","stored","in","the","leaves"]
code: def __init__(self, lefts, rights, features, thresholds, values):
      """
      Args:
          lefts: The id of the left nodes
          rights: The id of the right nodes
          feature: The features used to make decisions
          thresholds: The thresholds used in the decisions
          values: The value stored in the leaves
      """
      self.lefts = lefts
      self.rights = rights
      self.features = features
      self.thresholds = thresholds
      self.values = values
  
imports: import copy
  import numpy
  import torch
  from ._tree_implementations import TreeImpl
  from ._tree_implementations import GEMMDecisionTreeImpl, TreeTraversalDecisionTreeImpl, PerfectTreeTraversalDecisionTreeImpl
  from .None import constants
  from hummingbird.ml.exceptions import MissingConverter
label_desc: Use image node_id 1 to create a new TreeParameters object  with example: obj = TreeParameters(lefts, rights, features, thresholds, values)