code: def forward(self, img):
      device = img.device
  
      # get patches
  
      patches = self.to_patch(img)
      batch, num_patches, *_ = patches.shape
  
      # for indexing purposes
  
      batch_range = torch.arange(batch, device=device)[:, None]
  
      # get positions
  
      pos_emb = self.encoder.pos_embedding[:, 1 : (num_patches + 1)]
  
      # patch to encoder tokens and add positions
  
      tokens = self.patch_to_emb(patches)
      tokens = tokens + pos_emb
  
      # prepare mask tokens
  
      mask_tokens = repeat(
          self.mask_token, "d -> b n d", b=batch, n=num_patches
      )
      mask_tokens = mask_tokens + pos_emb
  
      # calculate of patches needed to be masked, and get positions (indices) to be masked
  
      num_masked = int(self.masking_ratio * num_patches)
      masked_indices = (
          torch.rand(batch, num_patches, device=device)
          .topk(k=num_masked, dim=-1)
          .indices
      )
      masked_bool_mask = (
          torch.zeros((batch, num_patches), device=device)
          .scatter_(-1, masked_indices, 1)
          .bool()
      )
  
      # mask tokens
  
      tokens = torch.where(
          masked_bool_mask[..., None], mask_tokens, tokens
      )
  
      # attend with vision transformer
  
      encoded = self.encoder.transformer(tokens)
  
      # get the masked tokens
  
      encoded_mask_tokens = encoded[batch_range, masked_indices]
  
      # small linear projection for predicted pixel values
  
      pred_pixel_values = self.to_pixels(encoded_mask_tokens)
  
      # get the masked patches for the final reconstruction loss
  
      masked_patches = patches[batch_range, masked_indices]
  
      # calculate reconstruction loss
  
      recon_loss = (
          F.l1_loss(pred_pixel_values, masked_patches) / num_masked
      )
      return recon_loss
  
file_path: vit-pytorch/vit_pytorch/simmim.py
imports: import torch
  from torch import nn
  import torch.nn.functional
  from einops import repeat
label_desc: Use image node_id 2 for calling the SimMIM obj's underlying member method code with example usage: obj.forward(img) and returns: recon_loss