code: def forward(self, net: Tensor) -> Tensor:
      num_batch = net.shape[0]
      tree_net = self.tree_proj(net)
  
      num_flat_prob = 2 * self._num_internals
      arange_args = 0, num_flat_prob * num_batch, num_flat_prob
      batch_indices = torch.arange(
          *arange_args, device=tree_net.device
      ).view(-1, 1)
  
      if self._fast:
          routes = Route.apply(
              tree_net,
              self.tree_arange,
              batch_indices,
              self.ones,
              self.increment_indices,
              self._num_tree,
              num_batch,
              self._tree_depth,
              self._num_internals,
          )
      else:
          shape = num_batch, -1, self._num_internals
          p_left = torch.sigmoid(tree_net).view(*shape).transpose(0, 1)
          p_right = 1.0 - p_left
          flat_probabilities = torch.cat(
              [p_left, p_right], dim=-1
          ).contiguous()
          flat_probabilities = flat_probabilities.view(
              self._num_tree, -1
          )
          current_indices = batch_indices + self.increment_indices[0]  # type: ignore
          flat_dim = flat_probabilities.shape[-1]
          tree_arange = self.tree_arange * flat_dim  # type: ignore
          routes = flat_probabilities.take(
              tree_arange + current_indices[None, ...]
          )
          for i in range(1, self._tree_depth + 1):
              current_indices = batch_indices + self.increment_indices[i]  # type: ignore
              current_indices = tree_arange + current_indices[None, ...]
              routes *= flat_probabilities.take(current_indices)
  
      features = routes.transpose(0, 1).contiguous().view(num_batch, -1)
      if self.leaves is None or self._output_dim is None:
          return features.view(num_batch, self._num_tree, -1)
      if self._is_regression or self._output_dim <= 1:
          outputs = features.mm(self.leaves)
      else:
          if self._fast:
              outputs = LeafAggregation.apply(features, self.leaves)
          else:
              leaves = F.softmax(self.leaves, dim=1)
              outputs = features.mm(leaves)
      return outputs / self._num_tree
  
imports: import math
  import torch
  import numpy
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Tuple
  from typing import Union
  from typing import Callable
  from typing import Optional
  from functools import partial
  from torch.nn import Module
  from cftool.misc import print_warning
  from .hooks import IBasicHook
label_desc: Use image node_id 2 for calling the DNDF obj's underlying member method code with example usage: obj.forward(net) and returns: unknown, features