code: def test_script_processor_without_role(
      exists_mock, isfile_mock, sagemaker_session
  ):
      with pytest.raises(ValueError):
          ScriptProcessor(
              image_uri=CUSTOM_IMAGE_URI,
              command=["python3"],
              instance_type="ml.m4.xlarge",
              instance_count=1,
              volume_size_in_gb=100,
              volume_kms_key="arn:aws:kms:us-west-2:012345678901:key/volume-kms-key",
              output_kms_key="arn:aws:kms:us-west-2:012345678901:key/output-kms-key",
              max_runtime_in_seconds=3600,
              base_job_name="my_sklearn_processor",
              env={"my_env_variable": "my_env_variable_value"},
              tags=[{"Key": "my-tag", "Value": "my-tag-value"}],
              network_config=NetworkConfig(
                  subnets=["my_subnet_id"],
                  security_group_ids=["my_security_group_id"],
                  enable_network_isolation=True,
                  encrypt_inter_container_traffic=True,
              ),
              sagemaker_session=sagemaker_session,
          )
  
imports: from __future__ import absolute_import
  import copy
  import pytest
  from mock import Mock, patch, MagicMock
  from packaging import version
  from sagemaker import LocalSession
  from sagemaker.dataset_definition.inputs import S3Input, DatasetDefinition, AthenaDatasetDefinition, RedshiftDatasetDefinition
  from sagemaker.processing import ProcessingInput, ProcessingOutput, Processor, ScriptProcessor, ProcessingJob
  from sagemaker.session_settings import SessionSettings
  from sagemaker.spark.processing import PySparkProcessor
  from sagemaker.sklearn.processing import SKLearnProcessor
  from sagemaker.pytorch.processing import PyTorchProcessor
  from sagemaker.tensorflow.processing import TensorFlowProcessor
  from sagemaker.workflow.pipeline_definition_config import PipelineDefinitionConfig
  from sagemaker.xgboost.processing import XGBoostProcessor
  from sagemaker.mxnet.processing import MXNetProcessor
  from sagemaker.network import NetworkConfig
  from sagemaker.processing import FeatureStoreOutput
  from sagemaker.fw_utils import UploadedCode
  from sagemaker.workflow.pipeline_context import PipelineSession, _PipelineConfig
  from sagemaker.workflow.functions import Join
  from sagemaker.workflow.execution_variables import ExecutionVariables
  from tests.unit import SAGEMAKER_CONFIG_PROCESSING_JOB
label_desc: Use image node_id 24 for calling a global function with example usage: test_script_processor_without_role(exists_mock, isfile_mock, sagemaker_session) without return types