docstr: Calculates the loss for the model.
  
  Args:
      output (Dict): The output dictionary from the model
      y (torch.Tensor): The target tensor
      tag (str): The tag to use for logging
  
  Returns:
      torch.Tensor: The loss value
code: def calculate_loss(
      self, output: Dict, y: torch.Tensor, tag: str
  ) -> torch.Tensor:
      """Calculates the loss for the model.
  
      Args:
          output (Dict): The output dictionary from the model
          y (torch.Tensor): The target tensor
          tag (str): The tag to use for logging
  
      Returns:
          torch.Tensor: The loss value
      """
      y_hat = output["logits"]
      reg_terms = [
          k for k, v in output.items() if "regularization" in k
      ]
      reg_loss = 0
      for t in reg_terms:
          # Log only if non-zero
          if output[t] != 0:
              reg_loss += output[t]
              self.log(
                  f"{tag}_{t}_loss",
                  output[t],
                  on_epoch=True,
                  on_step=False,
                  logger=True,
                  prog_bar=False,
              )
      if self.hparams.task == "regression":
          computed_loss = reg_loss
          for i in range(self.hparams.output_dim):
              _loss = self.loss(y_hat[:, i], y[:, i])
              computed_loss += _loss
              if self.hparams.output_dim > 1:
                  self.log(
                      f"{tag}_loss_{i}",
                      _loss,
                      on_epoch=True,
                      on_step=False,
                      logger=True,
                      prog_bar=False,
                  )
      else:
          # TODO loss fails with batch size of 1?
          computed_loss = (
              self.loss(y_hat.squeeze(), y.squeeze()) + reg_loss
          )
      self.log(
          f"{tag}_loss",
          computed_loss,
          on_epoch=(tag in ["valid", "test"]),
          on_step=(tag == "train"),
          # on_step=False,
          logger=True,
          prog_bar=True,
      )
      return computed_loss
  
imports: import importlib
  import warnings
  from abc import ABCMeta, abstractmethod
  from functools import partial
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
  import numpy
  import pytorch_lightning
  import torch
  import torch.nn
  import torchmetrics
  from omegaconf import DictConfig, OmegaConf
  from pandas import DataFrame
  from torch import Tensor
  from torch.optim import Optimizer
  from pytorch_tabular.models.common.heads import blocks
  from pytorch_tabular.models.common.layers import PreEncoded1dLayer
  from pytorch_tabular.utils import get_logger, reset_all_weights
label_desc: Use image node_id 10 for calling the BaseModel obj's underlying member method code with example usage: obj.calculate_loss(output, y, tag) and returns: computed_loss