code: def test_sklearn_with_all_parameters_via_run_args_called_twice(
      exists_mock,
      isfile_mock,
      botocore_resolver,
      sklearn_version,
      sagemaker_session,
  ):
      botocore_resolver.return_value.construct_endpoint.return_value = {
          "hostname": ECR_HOSTNAME
      }
  
      processor = SKLearnProcessor(
          role=ROLE,
          framework_version=sklearn_version,
          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,
      )
  
      run_args = processor.get_run_args(
          code="/local/path/to/processing_code.py",
          inputs=_get_data_inputs_all_parameters(),
          outputs=_get_data_outputs_all_parameters(),
          arguments=["--drop-columns", "'SelfEmployed'"],
      )
      processor.run(
          code=run_args.code,
          inputs=run_args.inputs,
          outputs=run_args.outputs,
          arguments=run_args.arguments,
          wait=True,
          logs=False,
          experiment_config={"ExperimentName": "AnExperiment"},
      )
  
      expected_args = _get_expected_args_all_parameters(
          processor._current_job_name
      )
  
      sklearn_image_uri = (
          "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:{}-cpu-py3"
      ).format(sklearn_version)
      expected_args["app_specification"]["ImageUri"] = sklearn_image_uri
  
      sagemaker_session.process.assert_called_with(**expected_args)
  
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 9 for calling a global function with example usage: test_sklearn_with_all_parameters_via_run_args_called_twice(exists_mock, isfile_mock, botocore_resolver, sklearn_version, sagemaker_session) without return types