code: def forward(self, predicted_patches, target, mask):
      p, c, mpv, bits, device = (
          self.patch_size,
          self.channels,
          self.max_pixel_val,
          self.output_channel_bits,
          target.device,
      )
      bin_size = mpv / (2**bits)
  
      # un-normalize input
      if exists(self.mean) and exists(self.std):
          target = target * self.std + self.mean
  
      # reshape target to patches
      target = target.clamp(max=mpv)  # clamp just in case
      avg_target = reduce(
          target, "b c (h p1) (w p2) -> b (h w) c", "mean", p1=p, p2=p
      ).contiguous()
  
      channel_bins = torch.arange(
          bin_size, mpv, bin_size, device=device
      )
      discretized_target = torch.bucketize(avg_target, channel_bins)
  
      bin_mask = (2**bits) ** torch.arange(0, c, device=device).long()
      bin_mask = rearrange(bin_mask, "c -> () () c")
  
      target_label = torch.sum(bin_mask * discretized_target, dim=-1)
  
      loss = F.cross_entropy(
          predicted_patches[mask], target_label[mask]
      )
      return loss
  
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 2 for calling the MPPLoss obj's underlying member method code with example usage: obj.forward(predicted_patches, target, mask) and returns: loss