code: def _get_data_outputs_all_parameters():
      return [
          ProcessingOutput(
              source="/container/path/",
              destination="s3://uri/",
              output_name="my_output",
              s3_upload_mode="EndOfJob",
          ),
          ProcessingOutput(
              output_name="feature_store_output",
              app_managed=True,
              feature_store_output=FeatureStoreOutput(
                  feature_group_name="FeatureGroupName"
              ),
          ),
      ]
  
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 43 for calling a global function with example usage: _get_data_outputs_all_parameters() and returns: list