code: def test_selection_config(self):
      selection_config = SelectionConfigV3(
          n_samples=1,
          strategies=[
              SelectionConfigV3Entry(
                  input=SelectionConfigV3EntryInput(
                      type=SelectionInputType.EMBEDDINGS
                  ),
                  strategy=SelectionConfigV3EntryStrategy(
                      type=SelectionStrategyTypeV3.DIVERSITY,
                      stopping_condition_minimum_distance=-1,
                  ),
              ),
              SelectionConfigV3Entry(
                  input=SelectionConfigV3EntryInput(
                      type=SelectionInputType.SCORES,
                      task="my-classification-task",
                      score="uncertainty_margin",
                  ),
                  strategy=SelectionConfigV3EntryStrategy(
                      type=SelectionStrategyTypeV3.WEIGHTS
                  ),
              ),
              SelectionConfigV3Entry(
                  input=SelectionConfigV3EntryInput(
                      type=SelectionInputType.METADATA,
                      key="lightly.sharpness",
                  ),
                  strategy=SelectionConfigV3EntryStrategy(
                      type=SelectionStrategyTypeV3.THRESHOLD,
                      threshold=20,
                      operation=SelectionStrategyThresholdOperation.BIGGER_EQUAL,
                  ),
              ),
              SelectionConfigV3Entry(
                  input=SelectionConfigV3EntryInput(
                      type=SelectionInputType.PREDICTIONS,
                      task="my_object_detection_task",
                      name=SelectionInputPredictionsName.CLASS_DISTRIBUTION,
                  ),
                  strategy=SelectionConfigV3EntryStrategy(
                      type=SelectionStrategyTypeV3.BALANCE,
                      target={"Ambulance": 0.2, "Bus": 0.4},
                  ),
              ),
          ],
      )
      config = DockerWorkerConfigV3(
          worker_type=DockerWorkerType.FULL, selection=selection_config
      )
  
      self._check_if_openapi_generated_obj_is_valid(config)
  
imports: import json
  import random
  from typing import Any, List
  from unittest import mock
  from unittest.mock import MagicMock
  import pytest
  from pydantic import ValidationError
  from pytest_mock import MockerFixture
  from lightly.api import ApiWorkflowClient, api_workflow_compute_worker
  from lightly.api.api_workflow_compute_worker import STATE_SCHEDULED_ID_NOT_FOUND, ComputeWorkerRunInfo, InvalidConfigurationError, _config_to_camel_case, _snake_to_camel_case, _validate_config
  from lightly.openapi_generated.swagger_client.api import DockerApi
  from lightly.openapi_generated.swagger_client.api_client import ApiClient
  from lightly.openapi_generated.swagger_client.models import DockerRunData, DockerRunScheduledData, DockerRunScheduledPriority, DockerRunScheduledState, DockerRunState, DockerWorkerConfigV3, DockerWorkerConfigV3Docker, DockerWorkerConfigV3DockerCorruptnessCheck, DockerWorkerConfigV3Lightly, DockerWorkerConfigV3LightlyCollate, DockerWorkerConfigV3LightlyLoader, DockerWorkerState, DockerWorkerType, SelectionConfigV3, SelectionConfigV3Entry, SelectionConfigV3EntryInput, SelectionConfigV3EntryStrategy, SelectionInputPredictionsName, SelectionInputType, SelectionStrategyThresholdOperation, SelectionStrategyTypeV3, TagData
  from lightly.openapi_generated.swagger_client.rest import ApiException
  from tests.api_workflow import utils
  from tests.api_workflow.mocked_api_workflow_client import MockedApiWorkflowSetup
label_desc: Use image node_id 14 for calling the TestApiWorkflowComputeWorker obj's underlying member method code with example usage: obj.test_selection_config() without return types