class_docstr: Class implementing the Perfect Tree Traversal strategy in PyTorch for tree-base models.
code: def _get_weights_and_biases(
      self, nodes_map, tree_depth, weight_0, weight_1, bias_0
  ):
      def depth_f_traversal(
          node, current_depth, node_id, leaf_start_id
      ):
          weight_0[node_id] = node.feature
          bias_0[node_id] = node.threshold
          current_depth += 1
          node_id += 1
  
          # Condition false (right sub-tree)
          if node.right.feature == -1:
              node_id += 2 ** (tree_depth - current_depth - 1) - 1
              v = node.right.value
              weight_1[
                  leaf_start_id : leaf_start_id
                  + 2 ** (tree_depth - current_depth - 1)
              ] = (
                  np.ones(
                      (
                          2 ** (tree_depth - current_depth - 1),
                          self.n_classes,
                      )
                  )
                  * v
              )
              leaf_start_id += 2 ** (tree_depth - current_depth - 1)
          else:
              node_id, leaf_start_id = depth_f_traversal(
                  node.right, current_depth, node_id, leaf_start_id
              )
  
          # Condition true (left sub-tree)
          if node.left.feature == -1:
              node_id += 2 ** (tree_depth - current_depth - 1) - 1
              v = node.left.value
              weight_1[
                  leaf_start_id : leaf_start_id
                  + 2 ** (tree_depth - current_depth - 1)
              ] = (
                  np.ones(
                      (
                          2 ** (tree_depth - current_depth - 1),
                          self.n_classes,
                      )
                  )
                  * v
              )
              leaf_start_id += 2 ** (tree_depth - current_depth - 1)
          else:
              node_id, leaf_start_id = depth_f_traversal(
                  node.left, current_depth, node_id, leaf_start_id
              )
  
          return node_id, leaf_start_id
  
      depth_f_traversal(nodes_map[0], -1, 0, 0)
  
imports: from abc import abstractmethod
  from enum import Enum
  import numpy
  import torch
  from .None import constants
  from ._physical_operator import PhysicalOperator
  from .None import _tree_commons
label_desc: Use image node_id 5 for calling the PerfectTreeTraversalTreeImpl obj's underlying member method code with example usage: obj._get_weights_and_biases(nodes_map, tree_depth, weight_0, weight_1, bias_0) and returns: node_id,  leaf_start_id