code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      if self.feature_mask_function.__name__ == "t_softmax":
          t = torch.relu(self.t)
          feature_mask = self.feature_mask_function(
              self.feature_mask, t
          )
      else:
          feature_mask = self.feature_mask_function(self.feature_mask)
      # Repeat W for each batch size using broadcasting
      W = torch.ones(x.size(0), 1, 1, device=x.device) * self.W
      # Binning features
      x = torch.bmm(x.unsqueeze(-1), W) - self.cut_points.unsqueeze(0)
      x = self.binning_activation(x)  # , dim=-1)
      x = x * self.leaf_responses.unsqueeze(0)
      x = (x * feature_mask.reshape(1, -1, 1)).sum(dim=1)
      return x, feature_mask
  
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 NeuralDecisionStump obj's underlying member method code with example usage: obj.forward(x) and returns: x,  feature_mask