code: def forward(self, x: Dict):
      if self.mode == "pretrain":
          x = self.embedding_layer(x)
          # (B, N, E)
          features = self.featurizer(x, perturb=True)
          z, mask = features.features, features.mask
          # decoder
          z_hat = self.decoder(z)
          # reconstruction
          reconstructed_in = self.reconstruction(z_hat)
          # mask reconstruction
          reconstructed_mask = self.mask_reconstruction(z_hat)
          output_dict = {
              "mask": self.output_tuple(mask, reconstructed_mask)
          }
          if "continuous" in reconstructed_in.keys():
              output_dict["continuous"] = self.output_tuple(
                  torch.cat(
                      [
                          i
                          for i in [
                              x.get("continuous", None),
                              x.get("embedding", None),
                          ]
                          if i is not None
                      ],
                      1,
                  ),
                  reconstructed_in["continuous"],
              )
          if "categorical" in reconstructed_in.keys():
              output_dict["categorical"] = self.output_tuple(
                  x["_categorical_orig"],
                  reconstructed_in["categorical"],
              )
          if "binary" in reconstructed_in.keys():
              output_dict["binary"] = self.output_tuple(
                  x["binary"], reconstructed_in["binary"]
              )
          return output_dict
      else:  # self.mode == "finetune"
          z, x = self.featurizer(x, perturb=False, return_input=True)
          if self.hparams.include_input_features_inference:
              return torch.cat([z.features, x], 1)
          else:
              return z.features
  
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 9 for calling the DenoisingAutoEncoderModel obj's underlying member method code with example usage: obj.forward(x) and returns: output_dict, torch, z