code: def __init__(
      self,
      transformer,
      patch_size,
      dim,
      output_channel_bits=3,
      channels=3,
      max_pixel_val=1.0,
      mask_prob=0.15,
      replace_prob=0.5,
      random_patch_prob=0.5,
      mean=None,
      std=None,
  ):
      super().__init__()
      self.transformer = transformer
      self.loss = MPPLoss(
          patch_size,
          channels,
          output_channel_bits,
          max_pixel_val,
          mean,
          std,
      )
  
      # extract patching function
      self.patch_to_emb = nn.Sequential(
          transformer.to_patch_embedding[1:]
      )
  
      # output transformation
      self.to_bits = nn.Linear(
          dim, 2 ** (output_channel_bits * channels)
      )
  
      # vit related dimensions
      self.patch_size = patch_size
  
      # mpp related probabilities
      self.mask_prob = mask_prob
      self.replace_prob = replace_prob
      self.random_patch_prob = random_patch_prob
  
      # token ids
      self.mask_token = nn.Parameter(
          torch.randn(1, 1, channels * patch_size**2)
      )
  
file_path: vit-pytorch/vit_pytorch/mpp.py
imports: import math
  import torch
  from torch import nn
  import torch.nn.functional
  from einops import rearrange, repeat, reduce
label_desc: Use image node_id 1 to create a new MPP object from inherited base classes: nn with example: obj = MPP(transformer, patch_size, dim, output_channel_bits, channels, max_pixel_val, mask_prob, replace_prob, random_patch_prob, mean, std)