code: def calculate_loss(self, y, classification_logits, y_hat, tag):
      cl_loss = self.classification_loss(
          classification_logits.squeeze(), y[:, 0].squeeze().long()
      )
      rg_loss = self.loss(y_hat, y[:, 1])
      self.log(
          f"{tag}_classification_loss",
          cl_loss,
          on_epoch=True,
          on_step=False,
          logger=True,
          prog_bar=False,
      )
      self.log(
          f"{tag}_regression_loss",
          cl_loss,
          on_epoch=True,
          on_step=False,
          logger=True,
          prog_bar=False,
      )
      computed_loss = cl_loss + rg_loss
      self.log(
          f"{tag}_loss",
          computed_loss,
          on_epoch=(tag == "valid"),
          on_step=(tag == "train"),
          # on_step=False,
          logger=True,
          prog_bar=True,
      )
      return computed_loss
  
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 8 for calling the MultiStageModel obj's underlying member method code with example usage: obj.calculate_loss(y, classification_logits, y_hat, tag) and returns: computed_loss