code: def prepare_for_distributed_training(self):
      logger.info(
          "DISTRIBUTED FSDP -- preparing model for distributed training"
      )
      if has_batchnorms(self.student):
          raise NotImplementedError
      # below will synchronize all student subnetworks across gpus:
      for k, v in self.student.items():
          self.teacher[k].load_state_dict(self.student[k].state_dict())
          student_model_cfg = self.cfg.compute_precision.student[k]
          self.student[k] = get_fsdp_wrapper(
              student_model_cfg, modules_to_wrap={BlockChunk}
          )(self.student[k])
          teacher_model_cfg = self.cfg.compute_precision.teacher[k]
          self.teacher[k] = get_fsdp_wrapper(
              teacher_model_cfg, modules_to_wrap={BlockChunk}
          )(self.teacher[k])
  
imports: from functools import partial
  import logging
  import torch
  from torch import nn
  from dinov2.loss import DINOLoss, iBOTPatchLoss, KoLeoLoss
  from dinov2.models import build_model_from_cfg
  from dinov2.layers import DINOHead
  from dinov2.utils.utils import has_batchnorms
  from dinov2.utils.param_groups import get_params_groups_with_decay, fuse_params_groups
  from dinov2.fsdp import get_fsdp_wrapper, ShardedGradScaler, get_fsdp_modules, reshard_fsdp_model
  from dinov2.models.vision_transformer import BlockChunk
label_desc: Use image node_id 10 for calling the SSLMetaArch obj's underlying member method code with example usage: obj.prepare_for_distributed_training() without return types