code: def test_pytorch_ddp_distribution_configuration(
      sagemaker_session,
      pytorch_ddp_framework_version,
      pytorch_ddp_py_version,
  ):
      test_instance_type = "ml.p4d.24xlarge"
      pytorch = _pytorch_estimator(
          sagemaker_session,
          framework_version=pytorch_ddp_framework_version,
          py_version=pytorch_ddp_py_version,
          distribution=DISTRIBUTION_PYTORCH_DDP_ENABLED,
          instance_type=test_instance_type,
      )
      actual_pytorch_ddp = pytorch._pytorch_distribution_configuration(
          distribution=pytorch.distribution
      )
      expected_torch_ddp = {
          "sagemaker_pytorch_ddp_enabled": True,
          "sagemaker_instance_type": test_instance_type,
      }
      assert actual_pytorch_ddp == expected_torch_ddp
  
imports: from __future__ import absolute_import
  import logging
  import json
  import os
  import pytest
  from mock import ANY, MagicMock, Mock, patch
  from packaging.version import Version
  from sagemaker import image_uris
  from sagemaker.pytorch import defaults
  from sagemaker.pytorch import PyTorch, PyTorchPredictor, PyTorchModel
  from sagemaker.instance_group import InstanceGroup
  from sagemaker.session_settings import SessionSettings
label_desc: Use image node_id 30 for calling a global function with example usage: test_pytorch_ddp_distribution_configuration(sagemaker_session, pytorch_ddp_framework_version, pytorch_ddp_py_version) without return types