code: def swap_bert_encoder(
      model, data_type, lib_path, remove_padding=False
  ):
      """
      Replace the encoder of the model with a custom encoder
      that uses the Faster Transformer library.
      """
      weights = EncoderWeights(
          model.config.num_hidden_layers,
          model.config.hidden_size,
          model.state_dict(),
      )
      weights.to_cuda()
      if data_type == "fp16":
          weights.to_half()
      elif data_type == "bf16":
          weights.to_bfloat16()
      lib_path = os.path.abspath(lib_path)
      enc = CustomEncoder(
          model.config.num_hidden_layers,
          model.config.num_attention_heads,
          model.config.hidden_size // model.config.num_attention_heads,
          weights,
          remove_padding=remove_padding,
          path=lib_path,
      )
      enc_ = torch.jit.script(enc)
      model.replace_encoder(enc_)
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/compilers/faster_transformer/bert/__init__.py
imports: import os
  from nebullvm.operations.optimizations.compilers.faster_transformer.bert.modeling_bert import BertModel
  from nebullvm.operations.optimizations.compilers.faster_transformer.bert.modeling_bert import CustomEncoder, EncoderWeights
  from nebullvm.operations.optimizations.compilers.utils import get_faster_transformer_repo_path
  from nebullvm.optional_modules.huggingface import BertModel
  from nebullvm.optional_modules.torch import torch
label_desc: Use image node_id 1 for calling a global function with example usage: swap_bert_encoder(model, data_type, lib_path, remove_padding) without return types