code: def convert_sync_bn(config, model):
      """
      Convert the BatchNorm layers in the model to the SyncBatchNorm layers.
  
      For SyncBatchNorm, we support two sources: Apex and PyTorch. The optimized
      SyncBN kernels provided by apex run faster.
  
      Args:
          config (AttrDict): configuration file
          model: Pytorch model whose BatchNorm layers should be converted to SyncBN
                 layers.
  
      NOTE: Since SyncBatchNorm layer synchronize the BN stats across machines, using
            the syncBN layer can be slow. In order to speed up training while using
            syncBN, we recommend using process_groups which are very well supported
            for Apex.
            To set the process groups, set SYNC_BN_CONFIG.GROUP_SIZE following below:
            1) if group_size=-1 -> use the VISSL default setting. We synchronize within a
               machine and hence will set group_size=num_gpus per node. This gives the best
               speedup.
            2) if group_size>0 -> will set group_size=value set by user.
            3) if group_size=0 -> no groups are created and process_group=None. This means
               global sync is done.
      """
      sync_bn_config = config.MODEL.SYNC_BN_CONFIG
  
      def get_group_size():
          world_size = (
              config.DISTRIBUTED.NUM_PROC_PER_NODE
              * config.DISTRIBUTED.NUM_NODES
          )
          if sync_bn_config["GROUP_SIZE"] > 0:
              # if the user specifies group_size to create, we use that.
              # we also make sure additionally that the group size doesn't exceed
              # the world_size. This is beneficial to handle especially in case
              # of 1 node training where num_gpu <= 8
              group_size = min(world_size, sync_bn_config["GROUP_SIZE"])
          elif sync_bn_config["GROUP_SIZE"] == 0:
              # group_size=0 is considered as world_size and no process group is created.
              group_size = None
          else:
              # by default, we set it to number of gpus in a node. Within gpu, the
              # interconnect is fast and syncBN is cheap.
              group_size = config.DISTRIBUTED.NUM_PROC_PER_NODE
          logging.info(f"Using SyncBN group size: {group_size}")
          return group_size
  
      def to_apex_syncbn(group_size):
          logging.info("Converting BN layers to Apex SyncBN")
          if group_size is None:
              process_group = None
              logging.info(
                  "Not creating process_group for Apex SyncBN..."
              )
          else:
              process_group = apex.parallel.create_syncbn_process_group(
                  group_size=group_size
              )
          return apex.parallel.convert_syncbn_model(
              model, process_group=process_group
          )
  
      def to_pytorch_syncbn(group_size):
          logging.info("Converting BN layers to PyTorch SyncBN")
          if group_size is None:
              process_group = None
              logging.info(
                  "Not creating process_group for PyTorch SyncBN..."
              )
          else:
              process_group_ids = split_world_in_process_groups(
                  world_size=config.DISTRIBUTED.NUM_PROC_PER_NODE
                  * config.DISTRIBUTED.NUM_NODES,
                  group_size=group_size,
              )
              process_groups = [
                  dist.new_group(pids) for pids in process_group_ids
              ]
              _, dist_rank = get_machine_local_and_dist_rank()
              process_group = process_groups[dist_rank // group_size]
          return nn.SyncBatchNorm.convert_sync_batchnorm(
              model, process_group=process_group
          )
  
      group_size = get_group_size()
      # Apply the correct transform, make sure that any other setting raises an error
      return {
          SyncBNTypes.apex: to_apex_syncbn,
          SyncBNTypes.pytorch: to_pytorch_syncbn,
      }[sync_bn_config["SYNC_BN_TYPE"]](group_size)
  
imports: import logging
  import math
  import warnings
  from enum import Enum
  from typing import Dict, List, Tuple
  import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
  from torch.nn.modules.utils import _ntuple
  from torch.utils.checkpoint import checkpoint
  from vissl.data.collators.collator_helper import MultiDimensionalTensor
  from vissl.utils.activation_checkpointing import checkpoint_trunk
  from vissl.utils.env import get_machine_local_and_dist_rank
  from vissl.utils.misc import is_apex_available
label_desc: Use image node_id 7 for calling a global function with example usage: convert_sync_bn(config, model) and returns: dict, group_size, apex, nn