code: def _get_data_inputs_all_parameters():
      return [
          ProcessingInput(
              source="s3://path/to/my/dataset/census.csv",
              destination="/container/path/",
              input_name="my_dataset",
              s3_data_type="S3Prefix",
              s3_input_mode="File",
              s3_data_distribution_type="FullyReplicated",
              s3_compression_type="None",
          ),
          ProcessingInput(
              input_name="s3_input",
              s3_input=S3Input(
                  s3_uri="s3://path/to/my/dataset/census.csv",
                  local_path="/container/path/",
                  s3_data_type="S3Prefix",
                  s3_input_mode="File",
                  s3_data_distribution_type="FullyReplicated",
                  s3_compression_type="None",
              ),
          ),
          ProcessingInput(
              input_name="redshift_dataset_definition",
              app_managed=True,
              dataset_definition=DatasetDefinition(
                  data_distribution_type="FullyReplicated",
                  input_mode="File",
                  local_path="/opt/ml/processing/input/dd",
                  redshift_dataset_definition=RedshiftDatasetDefinition(
                      cluster_id="cluster_id",
                      database="database",
                      db_user="db_user",
                      query_string="query_string",
                      cluster_role_arn="cluster_role_arn",
                      output_s3_uri="output_s3_uri",
                      kms_key_id="kms_key_id",
                      output_format="CSV",
                      output_compression="SNAPPY",
                  ),
              ),
          ),
          ProcessingInput(
              input_name="athena_dataset_definition",
              app_managed=True,
              dataset_definition=DatasetDefinition(
                  data_distribution_type="FullyReplicated",
                  input_mode="File",
                  local_path="/opt/ml/processing/input/dd",
                  athena_dataset_definition=AthenaDatasetDefinition(
                      catalog="catalog",
                      database="database",
                      query_string="query_string",
                      output_s3_uri="output_s3_uri",
                      work_group="workgroup",
                      kms_key_id="kms_key_id",
                      output_format="AVRO",
                      output_compression="ZLIB",
                  ),
              ),
          ),
      ]
  
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 42 for calling a global function with example usage: _get_data_inputs_all_parameters() and returns: list