code: def forward(
      self,
      img,
      mask: bool = True,
      layer_results: bool = False,
  ):
      x = self.patch_embed(img)
      batch_size, seq_len, _ = x.size()
  
      if mask:
          mask_indices = self.make_mask(
              img.size(0),
              self.cfg.num_mask_patches,
              self.cfg.min_mask_patches_per_block,
              self.cfg.max_mask_patches_per_block,
          )
          bool_mask = mask_indices.view(mask_indices.size(0), -1).bool()
      else:
          mask_indices = bool_mask = None
  
      cls_tokens = self.cls_emb.expand(batch_size, -1, -1)
      x = torch.cat((cls_tokens, x), dim=1)
  
      if self.ema is not None:
          with torch.no_grad():
              self.ema.model.eval()
  
              if self.cfg.ema_transformer_only:
                  y = self.ema.model(
                      x,
                      layer_results="end"
                      if self.cfg.end_of_block_targets
                      else "fc",
                  )
              else:
                  y = self.ema.model(
                      img,
                      mask=False,
                      layer_results=True,
                  )
  
          y = y[-self.cfg.average_top_k_layers :]
  
          permuted = False
          if (
              self.cfg.instance_norm_target_layer
              or self.cfg.batch_norm_target_layer
          ):
              y = [tl.transpose(1, 2) for tl in y]  # BTC -> BCT
              permuted = True
  
          if self.cfg.batch_norm_target_layer:
              y = [
                  F.batch_norm(
                      tl.float(),
                      running_mean=None,
                      running_var=None,
                      training=True,
                  )
                  for tl in y
              ]
  
          if self.cfg.instance_norm_target_layer:
              y = [F.instance_norm(tl.float()) for tl in y]
  
          if permuted:
              y = [tl.transpose(1, 2) for tl in y]  # BCT -> BTC
  
          if self.cfg.layer_norm_target_layer:
              y = [F.layer_norm(tl.float(), tl.shape[-1:]) for tl in y]
  
          y = sum(y) / len(y)
  
          if self.cfg.layer_norm_targets:
              y = F.layer_norm(y.float(), y.shape[-1:])
  
          if self.cfg.instance_norm_targets:
              y = F.instance_norm(y.float().transpose(1, 2)).transpose(
                  1, 2
              )
  
          y = y[bool_mask].float()
  
      if mask_indices is not None:
          mask_token = self.mask_emb.expand(batch_size, seq_len, -1)
          w = mask_indices.view(mask_indices.size(0), -1, 1).type_as(
              mask_token
          )
          x[:, 1:] = x[:, 1:] * (1 - w) + mask_token * w
  
      if layer_results:
          enc_layer_results = (
              "end" if self.cfg.end_of_block_targets else "fc"
          )
      else:
          enc_layer_results = None
  
      x = self.encoder(x, layer_results=enc_layer_results)
      if layer_results or mask_indices is None:
          return x
  
      x = x[bool_mask].float()
  
      if self.loss_beta == 0:
          loss = F.mse_loss(x, y, reduction="none").sum(dim=-1)
      else:
          loss = F.smooth_l1_loss(
              x, y, reduction="none", beta=self.loss_beta
          ).sum(dim=-1)
  
      if self.loss_scale > 0:
          loss = loss * self.loss_scale
  
      result = {
          "losses": {"regression": loss.sum()},
          "sample_size": loss.numel(),
          "target_var": self.compute_var(y),
          "pred_var": self.compute_var(x),
          "ema_decay": self.ema.get_decay() * 1000,
      }
      return result
  
imports: import logging
  import math
  import numpy
  import random
  from dataclasses import dataclass, field
  from typing import Optional
  from omegaconf import II
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.distributed
  from fairseq.modules import EMAModule, EMAModuleConfig
  from fairseq.dataclass import FairseqDataclass
  from fairseq.models import BaseFairseqModel, register_model
label_desc: Use image node_id 8 for calling the Data2VecVisionModel obj's underlying member method code with example usage: obj.forward(img, mask, layer_results) and returns: result, x