code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      tree_input = x
      feature_masks = []
      for d in range(self.depth):
          layer_nodes = []
          layer_feature_masks = []
          for n in range(max(2 ** (d), 1)):
              leaf_nodes, feature_mask = self._modules[
                  f"decision_stump_{d}_{n}"
              ](tree_input)
              layer_nodes.append(leaf_nodes)
              layer_feature_masks.append(feature_mask)
          layer_nodes = torch.cat(layer_nodes, dim=1)
          tree_input = torch.cat([x, layer_nodes], dim=1)
          feature_masks.append(layer_feature_masks)
      return self.dropout(layer_nodes), feature_masks
  
imports: import random
  from typing import Callable
  from warnings import warn
  import numpy
  import torch
  import torch.nn
  from pytorch_tabular.utils import check_numpy
  from ..layers import ModuleWithInit
  from .activations import RSoftmax, entmax15, sparsemax, sparsemoid
label_desc: Use image node_id 3 for calling the NeuralDecisionTree obj's underlying member method code with example usage: obj.forward(x) and returns: self,  feature_masks