code: def forward_decoder(self, x_encoded, idx_keep, idx_mask):
      # build decoder input
      batch_size = x_encoded.shape[0]
      x_decode = self.decoder.embed(x_encoded)
      x_masked = utils.repeat_token(
          self.mask_token, (batch_size, self.sequence_length)
      )
      x_masked = utils.set_at_index(
          x_masked, idx_keep, x_decode.type_as(x_masked)
      )
  
      # decoder forward pass
      x_decoded = self.decoder.decode(x_masked)
  
      # predict pixel values for masked tokens
      x_pred = utils.get_at_index(x_decoded, idx_mask)
      x_pred = self.decoder.predict(x_pred)
      return x_pred
  
file_path: lightly/examples/pytorch_lightning/mae.py
imports: import pytorch_lightning
  import torch
  import torchvision
  from torch import nn
  from lightly.models import utils
  from lightly.models.modules import masked_autoencoder
  from lightly.transforms.mae_transform import MAETransform
label_desc: Use image node_id 3 for calling the MAE obj's underlying member method code with example usage: obj.forward_decoder(x_encoded, idx_keep, idx_mask) and returns: x_pred