code: def calculate_loss(self, output, tag):
      total_loss = 0
      for type_, out in output.items():
          if type_ == "categorical":
              loss = 0
              for i in range(out.original.size(-1)):
                  loss += self.losses[type_](
                      out.reconstructed[i], out.original[:, i]
                  )
          elif type_ == "binary":
              # Casting output to float for BCEWithLogitsLoss
              loss = self.losses[type_](
                  out.reconstructed, out.original.float()
              )
          else:
              loss = self.losses[type_](out.reconstructed, out.original)
          loss *= getattr(self.loss_weights, type_)
          self.log(
              f"{tag}_{type_}_loss",
              loss.item(),
              on_epoch=True,
              on_step=False,
              logger=True,
              prog_bar=False,
          )
          total_loss += loss
      self.log(
          f"{tag}_loss",
          total_loss,
          on_epoch=(tag == "valid") or (tag == "test"),
          on_step=(tag == "train"),
          # on_step=False,
          logger=True,
          prog_bar=True,
      )
      return total_loss
  
imports: from collections import namedtuple
  from typing import Dict
  import torch
  import torch.nn
  from omegaconf import DictConfig
  from ..base_model import SSLBaseModel
  from ..common.heads import MultiTaskHead
  from ..common.layers import MixedEmbedding1dLayer
  from ..common.noise_generators import SwapNoiseCorrupter
label_desc: Use image node_id 10 for calling the DenoisingAutoEncoderModel obj's underlying member method code with example usage: obj.calculate_loss(output, tag) and returns: total_loss