code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      h = x
      t = (
          torch.relu(self.t)
          if self.feature_mask_function.__name__ == "t_softmax"
          else None
      )
      for d in range(self.n_stages):
          if self.feature_mask_function.__name__ == "t_softmax":
              feature = (
                  self.feature_mask_function(
                      self.feature_masks[d], t[d]
                  )
                  * x
              )
          else:
              feature = (
                  self.feature_mask_function(self.feature_masks[d]) * x
              )
          h_in = self.W_in[d](torch.cat([feature, h], dim=-1))
          z = torch.sigmoid(h_in[:, : self.n_features_in])
          r = torch.sigmoid(h_in[:, self.n_features_in :])  # noqa: E203
          h_out = torch.tanh(
              self.W_out[d](torch.cat([r * h, x], dim=-1))
          )
          h = self.dropout((1 - z) * h + z * h_out)
      return h
  
imports: import random
  from typing import Callable
  import torch
  import torch.nn
  from pytorch_tabular.models.common.layers.activations import RSoftmax, entmax15
label_desc: Use image node_id 4 for calling the GatedFeatureLearningUnit obj's underlying member method code with example usage: obj.forward(x) and returns: h