code: def test_estimator_use_kwargs(self):
      all_init_kwargs_used = {
          "image_uri": "blah1",
          "role": "str = None",
          "instance_count": 1,
          "instance_type": "ml.p2.xlarge",
          "keep_alive_period_in_seconds": 1,
          "volume_size": 30,
          "volume_kms_key": "Optional[Union[str, PipelineVariable]] = None",
          "max_run": 24 * 60 * 60,
          "input_mode": "File",
          "output_path": "Optional[Union[str, PipelineVariable]] = None",
          "output_kms_key": "Optional[Union[str, PipelineVariable]] = None",
          "base_job_name": "Optional[str] = None",
          "sagemaker_session": DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
          "hyperparameters": {"hyp1": "val1"},
          "tags": [],
          "subnets": ["1", "2"],
          "security_group_ids": ["1", "2"],
          "model_uri": "Optional[str] = None",
          "model_channel_name": "Union[str, PipelineVariable] = model",
          "metric_definitions": [{"1": "hum"}],
          "encrypt_inter_container_traffic": True,
          "use_spot_instances": False,
          "max_wait": 4,
          "checkpoint_s3_uri": "43",
          "checkpoint_local_path": "Optional[Union[str, PipelineVariable]] = None",
          "enable_network_isolation": True,
          "rules": ["RuleBase()"],
          "debugger_hook_config": True,
          "tensorboard_output_config": "TensorBoardOutputConfig()",
          "enable_sagemaker_metrics": True,
          "profiler_config": ProfilerConfig(),
          "disable_profiler": False,
          "environment": {"1": "2"},
          "max_retry_attempts": 4,
          "source_dir": "blah",
          "git_config": {"1", "3"},
          "container_log_level": 4,
          "code_location": "Optional[str] = None",
          "entry_point": "Optional[Union[str, PipelineVariable]] = None",
          "dependencies": ["Optional[List[str]] = None"],
          "instance_groups": [InstanceGroup()],
          "training_repository_access_mode": "Optional[Union[str, PipelineVariable]] = None",
          "training_repository_credentials_provider_arn": "Optional[Union[str, PipelineVariable]] = None",
      }
      all_fit_kwargs_used = {
          "inputs": {"hello": "world"},
          "wait": True,
          "logs": "All",
          "job_name": "none_of_your_business",
          "experiment_config": {"1": "2"},
      }
      all_deploy_kwargs_used = {
          "initial_instance_count": 88,
          "instance_type": "ml.p2.xlarge",
          "serializer": "BaseSerializer()",
          "deserializer": "BaseDeserializer()",
          "accelerator_type": "None",
          "endpoint_name": "None",
          "tags": [],
          "kms_key": "None",
          "wait": True,
          "data_capture_config": "None",
          "async_inference_config": "None",
          "serverless_inference_config": "None",
          "volume_size": 3,
          "model_data_download_timeout": 4,
          "container_startup_health_check_timeout": 2,
          "inference_recommendation_id": "None",
          "explainer_config": "None",
      }
  
      self.evaluate_estimator_workflow_with_kwargs(
          init_kwargs=all_init_kwargs_used,
          fit_kwargs=all_fit_kwargs_used,
          deploy_kwargs=all_deploy_kwargs_used,
      )
  
imports: from __future__ import absolute_import
  import time
  from typing import Optional, Set
  from unittest import mock
  import unittest
  from inspect import signature
  import pytest
  from sagemaker.async_inference.async_inference_config import AsyncInferenceConfig
  from sagemaker.debugger.profiler_config import ProfilerConfig
  from sagemaker.estimator import Estimator
  from sagemaker.instance_group import InstanceGroup
  from sagemaker.jumpstart.artifacts.environment_variables import _retrieve_default_environment_variables
  from sagemaker.jumpstart.artifacts.hyperparameters import _retrieve_default_hyperparameters
  from sagemaker.jumpstart.artifacts.metric_definitions import _retrieve_default_training_metric_definitions
  from sagemaker.jumpstart.constants import DEFAULT_JUMPSTART_SAGEMAKER_SESSION
  from sagemaker.jumpstart.enums import JumpStartScriptScope, JumpStartTag
  from sagemaker.jumpstart.estimator import JumpStartEstimator
  from sagemaker.jumpstart.utils import get_jumpstart_content_bucket
  from sagemaker.session_settings import SessionSettings
  from tests.integ.sagemaker.jumpstart.utils import get_training_dataset_for_model_and_version
  from sagemaker.model import Model
  from sagemaker.predictor import Predictor
  from tests.unit.sagemaker.jumpstart.utils import get_special_model_spec, overwrite_dictionary
label_desc: Use image node_id 8 for calling the EstimatorTest obj's underlying member method code with example usage: obj.test_estimator_use_kwargs() without return types