code: def execute(
      self,
      model: Module,
      train_input_data: DataManager,
      eval_input_data: DataManager,
      metric_drop_ths: float,
      metric: Callable,
  ):
      script_path = (
          Path(__file__).parent / "scripts/neural_magic_training.py"
      )
      with TemporaryDirectory(dir="") as tmp_dir:
          tmp_dir = Path(tmp_dir)
          requirements_file = tmp_dir / "requirements.txt"
          model_path = _save_model(model, tmp_dir)
          training_data_dir = tmp_dir / "train"
          eval_data_dir = tmp_dir / "eval"
          config_file = tmp_dir / "config.json"
          pruned_model_path = (
              tmp_dir / "pruned_model.pt"
              if model_path.is_file()
              else tmp_dir
          )
  
          _write_requirements_file(requirements_file)
          _save_dataset(train_input_data, training_data_dir)
          _save_dataset(eval_input_data, eval_data_dir)
          _save_json(self._config, config_file)
  
          run_in_different_venv(
              str(requirements_file),
              str(script_path),
              torch.cuda.is_available(),
              "--model",
              f"{model_path}",
              "--train_dir",
              f"{training_data_dir}",
              "--eval_dir",
              f"{eval_data_dir}",
              "--config",
              f"{config_file}",
              "--pruned_model",
              f"{pruned_model_path}",
          )
  
          self.compressed_model = _load_model(pruned_model_path)
  
          if self.compressed_model is not None:
              error = self._compute_error(
                  model, self.compressed_model, eval_input_data, metric
              )
              if error > metric_drop_ths:
                  self.compressed_model = None
              else:
                  self.new_metric_ths = metric_drop_ths - error
  
imports: import json
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from typing import Callable, Dict
  import numpy
  from loguru import logger
  from nebullvm.operations.optimizations.compressors.base import Compressor
  from nebullvm.optional_modules.torch import torch, Module
  from nebullvm.tools.data import DataManager
  from nebullvm.tools.pytorch import save_with_torch_fx, load_with_torch_fx
  from nebullvm.tools.venv import run_in_different_venv
label_desc: Use image node_id 1 for calling the SparseMLCompressor obj's underlying member method code with example usage: obj.execute(model, train_input_data, eval_input_data, metric_drop_ths, metric) without return types