code: def __init__(
      self,
      *,
      encoder,
      decoder_dim,
      masking_ratio=0.75,
      decoder_depth=1,
      decoder_heads=8,
      decoder_dim_head=64,
  ):
      super().__init__()
      assert (
          masking_ratio > 0 and masking_ratio < 1
      ), "masking ratio must be kept between 0 and 1"
      self.masking_ratio = masking_ratio
  
      # extract some hyperparameters and functions from encoder (vision transformer to be trained)
  
      self.encoder = encoder
      num_patches, encoder_dim = encoder.pos_embedding.shape[-2:]
  
      self.to_patch = encoder.to_patch_embedding[0]
      self.patch_to_emb = nn.Sequential(*encoder.to_patch_embedding[1:])
  
      pixel_values_per_patch = encoder.to_patch_embedding[
          2
      ].weight.shape[-1]
  
      # decoder parameters
      self.decoder_dim = decoder_dim
      self.enc_to_dec = (
          nn.Linear(encoder_dim, decoder_dim)
          if encoder_dim != decoder_dim
          else nn.Identity()
      )
      self.mask_token = nn.Parameter(torch.randn(decoder_dim))
      self.decoder = Transformer(
          dim=decoder_dim,
          depth=decoder_depth,
          heads=decoder_heads,
          dim_head=decoder_dim_head,
          mlp_dim=decoder_dim * 4,
      )
      self.decoder_pos_emb = nn.Embedding(num_patches, decoder_dim)
      self.to_pixels = nn.Linear(decoder_dim, pixel_values_per_patch)
  
file_path: vit-pytorch/vit_pytorch/mae.py
imports: import torch
  from torch import nn
  import torch.nn.functional
  from einops import repeat
  from vit_pytorch.vit import Transformer
label_desc: Use image node_id 1 to create a new MAE object from inherited base classes: nn with example: obj = MAE()