code: def skip_if_incompatible(gpu_instance_type, request):
      """
      These tests are for training compiler enabled images/estimators only.
      """
      region = integ.test_region()
      if region not in integ.TRAINING_COMPILER_SUPPORTED_REGIONS:
          pytest.skip(
              "SageMaker Training Compiler is not supported in this region"
          )
      if (
          gpu_instance_type == "ml.p3.16xlarge"
          and region not in integ.DATA_PARALLEL_TESTING_REGIONS
      ):
          pytest.skip(
              "Data parallel testing is not allowed in this region"
          )
      if (
          gpu_instance_type == "ml.p3.2xlarge"
          and region in integ.TRAINING_NO_P3_REGIONS
      ):
          pytest.skip("no ml.p3 instances in this region")
  
imports: from __future__ import absolute_import
  import os
  from packaging import version
  import pytest
  from sagemaker.huggingface import HuggingFace
  from sagemaker.huggingface import TrainingCompilerConfig
  from sagemaker.tensorflow import TensorFlow
  from sagemaker.tensorflow import TrainingCompilerConfig
  from sagemaker.pytorch import PyTorch
  from sagemaker.pytorch import TrainingCompilerConfig
  from tests import integ
  from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES
  from tests.integ.timeout import timeout
label_desc: Use image node_id 5 for calling a global function with example usage: skip_if_incompatible(gpu_instance_type, request) without return types