code: def _setup_loss(self):
      self.losses = {
          "binary": nn.BCEWithLogitsLoss(),
          "categorical": nn.CrossEntropyLoss(),
          "continuous": nn.MSELoss(),
          "mask": nn.BCEWithLogitsLoss(),
      }
      if self.hparams.loss_type_weights is None:
          self.loss_weights = self.loss_weight_tuple(
              *self._init_loss_weights()
          )
      else:
          self.loss_weights = self.loss_weight_tuple(
              *self.hparams.loss_type_weights,
              self.hparams.mask_loss_weight,
          )
  
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 6 for calling the DenoisingAutoEncoderModel obj's underlying member method code with example usage: obj._setup_loss() without return types