code: def _validate_repack_job_non_configurable_args(
      arguments: dict,
      expected_script_name: str,
      expected_model_archive: Union[str, dict, PipelineVariable],
      expected_entry_point: str,
      expected_dependencies: str,
  ):
      assert (
          arguments["HyperParameters"]["inference_script"]
          == expected_script_name
      )
      assert (
          arguments["HyperParameters"]["sagemaker_program"]
          == expected_entry_point
      )
      assert (
          arguments["HyperParameters"]["dependencies"]
          == expected_dependencies
      )
  
      model_archive = arguments["HyperParameters"]["model_archive"]
      if is_pipeline_variable(model_archive):
          assert model_archive.expr == expected_model_archive
      else:
          assert model_archive == expected_model_archive
  
imports: from __future__ import absolute_import
  import json
  import os
  import tempfile
  import shutil
  from typing import Union
  from mock import patch
  import pytest
  from mock.mock import MagicMock
  from sagemaker import Model, PipelineModel, Session, Processor
  from sagemaker.chainer import ChainerModel
  from sagemaker.estimator import Estimator
  from sagemaker.huggingface import HuggingFaceModel
  from sagemaker.model import SCRIPT_PARAM_NAME, DIR_PARAM_NAME
  from sagemaker.mxnet import MXNetModel
  from sagemaker.parameter import IntegerParameter
  from sagemaker.pytorch import PyTorchModel
  from sagemaker.sklearn import SKLearnModel
  from sagemaker.sparkml import SparkMLModel
  from sagemaker.tensorflow import TensorFlowModel
  from sagemaker.transformer import Transformer
  from sagemaker.tuner import HyperparameterTuner
  from sagemaker.workflow import _utils, is_pipeline_variable
  from sagemaker.workflow._utils import REPACK_SCRIPT_LAUNCHER
  from sagemaker.workflow.condition_step import ConditionStep
  from sagemaker.workflow.conditions import ConditionGreaterThanOrEqualTo
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.model_step import ModelStep, _REGISTER_MODEL_NAME_BASE, _CREATE_MODEL_NAME_BASE, _REPACK_MODEL_NAME_BASE
  from sagemaker.workflow.parameters import ParameterString, ParameterInteger
  from sagemaker.workflow.pipeline import Pipeline, PipelineGraph
  from sagemaker.workflow.pipeline_definition_config import PipelineDefinitionConfig
  from sagemaker.workflow.retry import StepRetryPolicy, StepExceptionTypeEnum, SageMakerJobExceptionTypeEnum, SageMakerJobStepRetryPolicy
  from sagemaker.xgboost import XGBoostModel
  from sagemaker.lambda_helper import Lambda
  from sagemaker.workflow.lambda_step import LambdaStep, LambdaOutput, LambdaOutputTypeEnum
  from tests.unit import DATA_DIR
  from tests.unit.sagemaker.workflow.helpers import CustomStep, ordered
  from tests.unit.sagemaker.workflow.conftest import BUCKET, ROLE, IMAGE_URI, INSTANCE_TYPE
label_desc: Use image node_id 20 for calling a global function with example usage: _validate_repack_job_non_configurable_args(arguments, expected_script_name, expected_model_archive, expected_entry_point, expected_dependencies) without return types