class_docstr: Class implementing the Tree Traversal strategy in PyTorch for tree-base models.
code: def forward(self, x):
      indexes = self._expand_indexes(x.size()[0])
  
      for _ in range(self.max_tree_depth):
          tree_nodes = indexes
          feature_nodes = torch.index_select(
              self.features, 0, tree_nodes
          ).view(-1, self.num_trees)
          feature_values = torch.gather(x, 1, feature_nodes)
  
          thresholds = torch.index_select(
              self.thresholds, 0, indexes
          ).view(-1, self.num_trees)
          lefts = torch.index_select(self.lefts, 0, indexes).view(
              -1, self.num_trees
          )
          rights = torch.index_select(self.rights, 0, indexes).view(
              -1, self.num_trees
          )
  
          indexes = torch.where(
              self.decision_cond(feature_values, thresholds),
              lefts,
              rights,
          ).long()
          indexes = indexes + self.nodes_offset
          indexes = indexes.view(-1)
  
      output = torch.index_select(self.values, 0, indexes).view(
          -1, self.num_trees, self.n_classes
      )
  
      output = self.aggregation(output)
  
      if self.regression:
          return output
  
      if self.anomaly_detection:
          # Select the class (-1 if negative) and return the score.
          return (
              torch.where(
                  output.view(-1) < 0, self.classes[0], self.classes[1]
              ),
              output,
          )
  
      if self.perform_class_select:
          return (
              torch.index_select(
                  self.classes, 0, torch.argmax(output, dim=1)
              ),
              output,
          )
      else:
          return torch.argmax(output, dim=1), output
  
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 4 for calling the TreeTraversalTreeImpl obj's underlying member method code with example usage: obj.forward(x) and returns: output, torch,  output, torch,  output, torch,  output