code: def forward(
      self, x: Dict, perturb: bool = True, return_input: bool = False
  ):
      # (B, N, E)
      x = self._concatenate_features(x)
      mask = None
      if perturb:
          # swap noise
          with torch.no_grad():
              x, mask = self.swap_noise(x)
      # encoder
      z = self.encoder(x)
      if return_input:
          return self.output_tuple(z, mask), x
      else:
          return self.output_tuple(z, mask)
  
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 DenoisingAutoEncoderFeaturizer obj's underlying member method code with example usage: obj.forward(x, perturb, return_input) and returns: self,  x, self