code: def test_torch_dynamo_fp32(
      output_library: DeepLearningFramework,
      dynamic: bool,
      quantization_type: QuantizationType,
      metric_drop_ths: int,
      metric: str,
  ):
      run_test_torch_dynamo(
          output_library,
          dynamic,
          quantization_type,
          metric_drop_ths,
          metric,
      )
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/tests/test_torch_dynamo.py
imports: import platform
  from tempfile import TemporaryDirectory
  import pytest
  import torch
  from nebullvm.core.models import DeviceType, Device, DeepLearningFramework, QuantizationType, ModelCompiler
  from nebullvm.operations.inference_learners.torch_dynamo import TorchDynamoInferenceLearner
  from nebullvm.operations.optimizations.compilers.torch_dynamo import TorchDynamoCompiler
  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.tools.utils import gpu_is_available, check_module_version
label_desc: Use image node_id 2 for calling a global function with example usage: test_torch_dynamo_fp32(output_library, dynamic, quantization_type, metric_drop_ths, metric) without return types