code: def _build_network(self):
      for d in range(self.depth):
          for n in range(max(2 ** (d), 1)):
              self.add_module(
                  f"decision_stump_{d}_{n}",
                  NeuralDecisionStump(
                      self.n_features + (2 ** (d) if d > 0 else 0),
                      self.binning_activation,
                      self.feature_mask_function,
                      self.feature_sparsity,
                      self.learnable_sparsity,
                  ),
              )
      self.dropout = nn.Dropout(self._dropout)
  
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 2 for calling the NeuralDecisionTree obj's underlying member method code with example usage: obj._build_network() without return types