code: def _compile_model(
      self,
      model: Union[Module, GraphModule],
      onnx_output_path: str,
      input_data: DataManager,
      model_params: ModelParams,
  ) -> str:
      self.conversion_op.model_name = "model_pruned"
      onnx_pruned_path = Path(onnx_output_path)
      self.conversion_op.to(self.device).set_state(
          model, input_data
      ).execute(onnx_pruned_path, model_params)
      onnx_pruned_path = str(onnx_pruned_path / "model_pruned.onnx")
      return onnx_pruned_path
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/compilers/deepsparse.py
imports: from pathlib import Path
  from typing import Union
  from nebullvm.core.models import ModelParams, QuantizationType
  from nebullvm.operations.conversions.converters import PytorchConverter
  from nebullvm.operations.optimizations.compilers.base import Compiler
  from nebullvm.optional_modules.torch import Module, GraphModule
  from nebullvm.tools.data import DataManager
label_desc: Use image node_id 3 for calling the DeepSparseCompiler obj's underlying member method code with example usage: obj._compile_model(model, onnx_output_path, input_data, model_params) and returns: onnx_pruned_path