code: def _build_network(self):
      self.embedding_layers = nn.ModuleList(
          [nn.Embedding(x, y) for x, y in self.hparams.embedding_dims]
      )
      if (
          self.hparams.embedding_dropout != 0
          and self.hparams.embedded_cat_dim != 0
      ):
          self.embedding_dropout = nn.Dropout(
              self.hparams.embedding_dropout
          )
      self.hparams.node_input_dim = (
          self.hparams.continuous_dim + self.hparams.embedded_cat_dim
      )
      self.backbone = NODEBackbone(self.hparams)
      # average first n channels of every tree, where n is the number of output targets for regression
      # and number of classes for classification
  
      def subset_clf(x):
          return x[..., :2].mean(dim=-2)
  
      def subset_rg(x):
          return x[..., 2:4].mean(dim=-2)
  
      # self.clf_out = utils.Lambda(subset_clf)
      # self.rg_out = utils.Lambda(subset_rg)
      self.classification_loss = nn.CrossEntropyLoss()
  
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 2 for calling the MultiStageModel obj's underlying member method code with example usage: obj._build_network() and returns: x, x