code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      if self.gflu_stages > 0:
          x = self.gflus(x)
      if self.num_trees > 0:
          # Decision Tree
          tree_outputs = []
          tree_feature_masks = (
              []
          )  # TODO make this optional and create feat importance
          tree_input = x
          for i in range(self.num_trees):
              tree_output, feat_masks = self.trees[i](tree_input)
              tree_outputs.append(tree_output.unsqueeze(-1))
              tree_feature_masks.append(feat_masks)
              if self.chain_trees:
                  tree_input = torch.cat([tree_input, tree_output], 1)
          tree_outputs = torch.cat(tree_outputs, dim=-1)
          if self.tree_wise_attention:
              tree_outputs = tree_outputs.permute(2, 0, 1)
              tree_outputs, _ = self.tree_attention(
                  tree_outputs, tree_outputs, tree_outputs
              )
              tree_outputs = tree_outputs.permute(1, 2, 0)
          return tree_outputs
      else:
          return x
  
imports: import torch
  import torch.nn
  from omegaconf import DictConfig
  from pytorch_tabular.models.common.heads import blocks
  from pytorch_tabular.models.common.layers import Add, Embedding1dLayer, GatedFeatureLearningUnit, NeuralDecisionTree
  from pytorch_tabular.models.common.layers.activations import entmax15, entmoid15, sparsemax, sparsemoid, t_softmax
  from pytorch_tabular.utils import get_logger
  from ..base_model import BaseModel
label_desc: Use image node_id 4 for calling the GatedAdditiveTreesBackbone obj's underlying member method code with example usage: obj.forward(x) and returns: tree_outputs, x