code: def forward(self, x: Dict):
      x = self.unpack_input(x)
      if (
          self.hparams.embedding_dropout != 0
          and self.hparams.embedded_cat_dim != 0
      ):
          x = self.embedding_dropout(x)
      x = self.backbone(x)
      clf_logits = self.clf_out(x)
      clf_prob = nn.functional.gumbel_softmax(clf_logits, tau=1, dim=-1)
  
      rg_out = self.rg_out(x)
  
      y_hat = torch.sum(clf_prob * rg_out, dim=-1)
      if (self.hparams.task == "regression") and (
          self.hparams.target_range is not None
      ):
          for i in range(self.hparams.output_dim):
              y_min, y_max = self.hparams.target_range[i]
              y_hat[:, i] = y_min + nn.Sigmoid()(y_hat[:, i]) * (
                  y_max - y_min
              )
      return {"logits": y_hat, "clf_logits": clf_logits}
  
imports: from dataclasses import dataclass, field
  from typing import Dict, List, Optional
  import pandas
  import torch
  import torch.nn
  from omegaconf import DictConfig
  from pytorch_tabular.config import DataConfig, ModelConfig, OptimizerConfig, TrainerConfig
  from pytorch_tabular.models.node import NODEBackbone
  from pytorch_tabular.tabular_model import TabularModel
  from sklearn.datasets import fetch_california_housing
  from pytorch_tabular.models import BaseModel
label_desc: Use image node_id 4 for calling the MultiStageModel obj's underlying member method code with example usage: obj.forward(x) and returns: dict