code: def validate_torch_distributed_distribution(
      instance_type,
      distribution,
      framework_version,
      py_version,
      image_uri,
      entry_point,
  ):
      """Check if torch_distributed distribution strategy is correctly invoked by the user.
  
      Args:
          instance_type (str): A string representing the type of training instance selected.
          distribution (dict): A dictionary with information to enable distributed training.
              (Defaults to None if distributed training is not enabled.) For example:
  
              .. code:: python
  
                  {
                      "torch_distributed": {
                          "enabled": True
                      }
                  }
          framework_version (str): A string representing the framework version selected.
          py_version (str): A string representing the python version selected.
          image_uri (str): A string representing a Docker image URI.
          entry_point (str or PipelineVariable): The absolute or relative path to the local Python
              source file that should be executed as the entry point to
              training.
  
      Raises:
          ValueError: if
              `py_version` is not python3 or
              `framework_version` is not compatible with instance types
      """
      torch_distributed_enabled = False
      if "torch_distributed" in distribution:
          torch_distributed_enabled = distribution.get(
              "torch_distributed"
          ).get("enabled", False)
      if not torch_distributed_enabled:
          # Distribution strategy other than torch_distributed is selected
          return
  
      err_msg = ""
  
      if not image_uri:
          # ignore framework_version and py_version if image_uri is set
          # in case image_uri is not set, then both are mandatory
          if "py3" not in py_version:
              err_msg += (
                  f"Provided py_version {py_version} is not supported by torch_distributed.\n"
                  "Please specify py_version>=py3\n"
              )
  
          # Check instance and framework_version compatibility
          if _is_gpu_instance(instance_type):
              if (
                  framework_version
                  not in TORCH_DISTRIBUTED_GPU_SUPPORTED_FRAMEWORK_VERSIONS
              ):
                  err_msg += (
                      f"Provided framework_version {framework_version} is not supported by"
                      f" torch_distributed for instance {instance_type}.\n"
                      "Please specify one of the supported framework versions:"
                      f"{TORCH_DISTRIBUTED_GPU_SUPPORTED_FRAMEWORK_VERSIONS} \n"
                  )
          elif _is_trainium_instance(instance_type):
              if (
                  framework_version
                  not in TRAINIUM_SUPPORTED_TORCH_DISTRIBUTED_FRAMEWORK_VERSIONS
              ):
                  err_msg += (
                      f"Provided framework_version {framework_version} is not supported by"
                      f" torch_distributed for instance {instance_type}.\n"
                      "Please specify one of the supported framework versions:"
                      f"{TRAINIUM_SUPPORTED_TORCH_DISTRIBUTED_FRAMEWORK_VERSIONS} \n"
                  )
          else:
              err_msg += "Currently torch_distributed is supported only for GPU and Trainium instances.\n"
  
      # Check entry point type
      if not entry_point.endswith(".py"):
          err_msg += (
              "Unsupported entry point type for the distribution torch_distributed.\n"
              "Only python programs (*.py) are supported."
          )
  
      if err_msg:
          raise ValueError(err_msg)
  
imports: from __future__ import absolute_import
  import json
  import logging
  import os
  import re
  import time
  import shutil
  import tempfile
  from collections import namedtuple
  from typing import List, Optional, Union, Dict
  from packaging import version
  import sagemaker.image_uris
  from sagemaker.instance_group import InstanceGroup
  from sagemaker.s3_utils import s3_path_join
  from sagemaker.session_settings import SessionSettings
  import sagemaker.utils
  from sagemaker.workflow import is_pipeline_variable
  from sagemaker.deprecations import renamed_warning, renamed_kwargs
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.deprecations import deprecation_warn_base
label_desc: Use image node_id 18 for calling a global function with example usage: validate_torch_distributed_distribution(instance_type, distribution, framework_version, py_version, image_uri, entry_point) without return types