code: def _compile_model(
      self,
      model: Union[Module, GraphModule],
      input_data: DataManager,
      quantization_type: QuantizationType,
  ) -> ScriptModule:
      model = deepcopy(
          model
      )  # Some operations modify the model in-place
      if isinstance(model, PyTorchTransformerWrapper):
          # .core_model is a huggingface model
          data_type = (
              "fp16"
              if quantization_type is QuantizationType.HALF
              else "fp32"
          )
          model.core_model = detect_and_swap_model(
              model.core_model,
              data_type=data_type,
              remove_padding=False,
          )
          if self.device.type is DeviceType.GPU:
              model.cuda()
  
      return super()._compile_model(
          model, input_data, quantization_type
      )
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/compilers/faster_transformer/__init__.py
imports: from copy import deepcopy
  from typing import Union
  from nebullvm.core.models import QuantizationType, DeviceType
  from nebullvm.operations.optimizations.compilers.faster_transformer.bert import detect_and_swap_bert_model
  from nebullvm.operations.optimizations.compilers.torchscript import TorchScriptCompiler
  from nebullvm.operations.optimizations.compilers.utils import get_faster_transformer_repo_path
  from nebullvm.optional_modules.torch import GraphModule, Module, ScriptModule, torch
  from nebullvm.tools.data import DataManager
  from nebullvm.tools.huggingface import PyTorchTransformerWrapper
label_desc: Use image node_id 1 for calling the FasterTransformerCompiler obj's underlying member method code with example usage: obj._compile_model(model, input_data, quantization_type) and returns: super