code: def test_torchscript_int8_quantization(
      output_library: DeepLearningFramework,
      dynamic: bool,
      quantization_type: QuantizationType,
      metric_drop_ths: int,
      metric: str,
  ):
      run_test_torchscript(
          output_library,
          dynamic,
          quantization_type,
          metric_drop_ths,
          metric,
      )
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/tests/test_torchscript.py
imports: from tempfile import TemporaryDirectory
  import pytest
  import torch
  from nebullvm.core.models import DeviceType, Device, DeepLearningFramework, QuantizationType, ModelCompiler
  from nebullvm.operations.inference_learners.torchscript import TorchScriptInferenceLearner
  from nebullvm.operations.optimizations.compilers.torchscript import TorchScriptCompiler
  from nebullvm.operations.optimizations.optimizers.base import COMPILER_TO_INFERENCE_LEARNER_MAP
  from nebullvm.operations.optimizations.tests.utils import initialize_model, check_model_validity
  from nebullvm.operations.inference_learners.utils import load_model
  from nebullvm.tools.utils import gpu_is_available
label_desc: Use image node_id 4 for calling a global function with example usage: test_torchscript_int8_quantization(output_library, dynamic, quantization_type, metric_drop_ths, metric) without return types