code: def __init__(self, vit):
      super().__init__()
  
      decoder_dim = 512
      self.mask_ratio = 0.75
      self.patch_size = vit.patch_size
      self.sequence_length = vit.seq_length
      self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_dim))
      self.backbone = masked_autoencoder.MAEBackbone.from_vit(vit)
      self.decoder = masked_autoencoder.MAEDecoder(
          seq_length=vit.seq_length,
          num_layers=1,
          num_heads=16,
          embed_input_dim=vit.hidden_dim,
          hidden_dim=decoder_dim,
          mlp_dim=decoder_dim * 4,
          out_dim=vit.patch_size**2 * 3,
          dropout=0,
          attention_dropout=0,
      )
  
file_path: lightly/examples/pytorch/mae.py
imports: 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 1 to create a new MAE object from inherited base classes: nn with example: obj = MAE(vit)