Remove node info from ML team serverless responses

[droberts195]

Serverless will present the impression of having no underlying servers,
so API responses in serverless should not contain any node information.

To avoid changing the shape of responses, we still return node fields
but always set to "serverless" in serverless. This avoids causing
problems for users who want to use the stateful client with serverless.

Trained model stats will be aggregated and reported as being for a
virtual node called "serverless".

[davidkyle]

LGTM

[davidkyle]

There are useful stats in this object so of which could be aggregated and lifted up to the top level as we have done with inference_count below.

We will want to review what model stats are presented in serverless, I'll open an issue for that

[droberts195]

Thanks.

The ES team is doing something similar for enrich stats - see elastic/elasticsearch#97472.

[github-actions]

Following you can find the validation results for the APIs you have changed.

API	Status	Request	Response
ml.clear_trained_model_deployment_cache	⚪	Missing test	Missing test
ml.close_job	🟢	62/62	61/61
ml.delete_calendar_event	🟢	4/4	4/4
ml.delete_calendar_job	🟢	3/3	3/3
ml.delete_calendar	🟢	5/5	5/5
ml.delete_data_frame_analytics	🟢	2/2	2/2
ml.delete_datafeed	🟢	3/3	3/3
ml.delete_expired_data	🟢	5/5	5/5
ml.delete_filter	🟢	27/27	27/27
ml.delete_forecast	🟢	3/3	3/3
ml.delete_job	🟢	47/47	47/47
ml.delete_model_snapshot	🟢	2/2	2/2
ml.delete_trained_model_alias	🟢	3/3	3/3
ml.delete_trained_model	🟢	3/3	3/3
ml.estimate_model_memory	🟢	16/16	16/16
ml.evaluate_data_frame	🟢	22/22	22/22
ml.explain_data_frame_analytics	🟢	7/7	7/7
ml.flush_job	🟢	15/15	15/15
ml.forecast	🟢	1/1	1/1
ml.get_buckets	🟢	14/14	14/14
ml.get_calendar_events	🟢	17/17	17/17
ml.get_calendars	🟢	17/17	17/17
ml.get_categories	🟢	12/12	12/12
ml.get_data_frame_analytics_stats	🟢	12/12	12/12
ml.get_data_frame_analytics	🟢	17/17	17/17
ml.get_datafeed_stats	🟢	27/27	27/27
ml.get_datafeeds	🟢	20/20	20/20
ml.get_filters	🟢	13/13	13/13
ml.get_influencers	🟢	11/11	11/11
ml.get_job_stats	🟢	31/31	31/31
ml.get_jobs	🟢	31/31	31/31
ml.get_memory_stats	⚪	Missing test	Missing test
ml.get_model_snapshot_upgrade_stats	🟢	3/3	3/3
ml.get_model_snapshots	🟢	18/18	18/18
ml.get_overall_buckets	🟢	16/16	15/15
ml.get_records	🟢	8/8	8/8
ml.get_trained_models_stats	🟢	12/12	12/12
ml.get_trained_models	🔴	21/27	27/27
ml.infer_trained_model	⚪	Missing test	Missing test
ml.info	🟢	10/10	10/10
ml.open_job	🟢	83/83	83/83
ml.post_calendar_events	🟢	21/21	21/21
ml.post_data	🔴	8/10	13/17
ml.preview_data_frame_analytics	🟢	3/3	3/3
ml.preview_datafeed	🔴	10/16	16/16
ml.put_calendar_job	🔴	11/12	12/12
ml.put_calendar	🟢	135/135	135/135
ml.put_data_frame_analytics	🟢	33/33	33/33
ml.put_datafeed	🔴	70/71	71/71
ml.put_filter	🟢	27/27	27/27
ml.put_job	🔴	218/226	224/224
ml.put_trained_model_alias	🟢	9/9	9/9
ml.put_trained_model_definition_part	⚪	Missing test	Missing test
ml.put_trained_model_vocabulary	⚪	Missing test	Missing test
ml.put_trained_model	🟢	5/5	5/5
ml.reset_job	🟢	2/2	2/2
ml.revert_model_snapshot	🟢	2/2	2/2
ml.set_upgrade_mode	🟢	6/6	6/6
ml.start_data_frame_analytics	🟢	1/1	1/1
ml.start_datafeed	🟢	24/24	24/24
ml.start_trained_model_deployment	⚪	Missing test	Missing test
ml.stop_data_frame_analytics	🟢	5/5	5/5
ml.stop_datafeed	🟢	17/17	17/17
ml.stop_trained_model_deployment	⚪	Missing test	Missing test
ml.update_data_frame_analytics	🟢	2/2	2/2
ml.update_datafeed	🟢	7/7	7/7
ml.update_filter	🟢	3/3	3/3
ml.update_job	🔴	4/5	5/5
ml.update_model_snapshot	🟢	3/3	3/3
ml.update_trained_model_deployment	🟠	Missing type	Missing type
ml.upgrade_job_snapshot	🟢	3/3	3/3
ml.validate_detector	🟢	2/2	2/2
ml.validate	🟢	3/3	3/3
transform.get_transform_stats	🟢	31/31	31/31

You can validate these APIs yourself by using the make validate target.