Adds e2e test for AutoML Forecasting and unit test for `TimeSeriesDataset`. Also adds `create_request_timeout` to `TimeSeriesDataset`, which #1099 seems to have missed.

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Fixes #<issue_number_goes_here> 🦕

… job (#1255)

Adds support for hierarchical forecasting and window filtering.

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Fixes b/229907889 b/228499154 🦕

* chore: release 1.13.1

Release-As: 1.13.1

* Changed test to use list_models() correctly

* feat: Changed system test to use list_models() correctly

Co-authored-by: Sam Goodman <[email protected]>

Add holiday regions for Vertex Forecasting.

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Fixes b/232964228 🦕

#1210)

* Added dataset snippets

* Fixed typehint and missing parameter bugs as well as added new samples

* Fixed lint issues

* Added bq batch_prediction bq snippets

* Removed unneeded fixture

* Renamed bq_source to bigquery_source

* Added back explain_tabular_sample.py for now

* Fixed tests

* Fixed lint issues

Co-authored-by: Rosie Zou <[email protected]>

* chore(ruby): Initial generation of google-iam-v1

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* feat: add display_name and metadata to ModelEvaluation in aiplatform model_evaluation.proto

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* docs: fix docstring formatting

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* feat: add Examples to Explanation related messages in aiplatform v1beta1 explanation.proto

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* feat: update protos to include InvalidateApprovalRequest and GetAccessApprovalServiceAccount APIs

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* chore: remove unused imports

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* feat: add latent_space_source to ExplanationMetadata in aiplatform v1 explanation_metadata.proto
feat: add scaling to OnlineServingConfig in aiplatform v1 featurestore.proto
feat: add template_metadata to PipelineJob in aiplatform v1 pipeline_job.proto

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* feat: add failure_policy to PipelineJob in aiplatform v1 & v1beta1 pipeline_job.proto

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* feat: add IAM policy to aiplatform_v1beta1.yaml
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feat: add successful_forecast_point_count to CompletionStats in completion_stats.proto

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* chore: use gapic-generator-python 1.0.0

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* feat: Client libraries for the Dataform API

This is the first release of the Public Dataform API client libraries.

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* Add batch_size kwarg for batch prediction jobs

* Fix errors

Update the copyright year. Change the order of the argument. Fix the syntax error.

* fix: change description layout

* feat: add clone method to PipelineJob

* fix: blacken and lint

* Update pipeline_jobs.py

* fix: update library names

* fix: formatting error

Thank you for opening a Pull Request! Before submitting your PR, there are a few things you can do to make sure it goes smoothly:
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Fixes b/229909845 🦕

---

Adds a `SequenceToSequencePlusForecastingTrainingJob` to training jobs. This job has the exact same signature as `AutoMLForecastingTrainingJob`, but we are creating a separate job in case the two models diverge in the future.

The logic for `AutoMLForecastingTrainingJob` has been moved to a new abstract base class `_ForecastingTrainingJob`. The only things that differ between the seq2seq and automl training jobs that extend it are the `model_type` and `training_task_definition`.

* fix: change endpoint update method to return resource

* fix: update unit tests for endpoint.update

* Pinned protobuf to prevent issues with pb files.

Currently, we rely on dependencies like proto-plus-python to select a protobuf library version.

However, if they select an incompatible version, it can lead to issues like #1257

* fix: Added upper limits for proto-plus + packaging

* Added testing constraint

Co-authored-by: Anthonios Partheniou <[email protected]>
Co-authored-by: Sam Goodman <[email protected]>

* resolve merge conflict

* fix: update unit tests for endpoint.update

* chore: minor fix to uploader test import

* resolve merge conflict

* fix: update unit tests for endpoint.update

* chore: update mock to use tf.__version__ directly

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