code: def replace_syncbn_with_syncbnfp32(
      opts, num_features: int
  ) -> nn.Module:
      # Sync-BN with 0 batch size does not work well with AMP. To avoid that,
      # we replace all sync_bn in mask rcnn head with FP32 ones.
      norm_layer = getattr(opts, "model.normalization.name", None)
  
      if norm_layer.find("sync") > -1:
          return get_normalization_layer(
              opts,
              num_features=num_features,
              norm_type="sync_batch_norm_fp32",
          )
      else:
          return get_normalization_layer(
              opts=opts, num_features=num_features
          )
  
imports: from typing import List, Tuple
  import torch
  from torch import Tensor, nn
  from cvnets.layers import ConvLayer2d, LinearLayer, TransposeConvLayer2d, get_normalization_layer
  from cvnets.misc.init_utils import initialize_conv_layer, initialize_fc_layer
label_desc: Use image node_id 1 for calling a global function with example usage: replace_syncbn_with_syncbnfp32(opts, num_features) and returns: get_normalization_layer, get_normalization_layer