Regenerate protos using protoc 3.19.4

[harupy]

Signed-off-by: harupy hkawamura0130@gmail.com

Related Issues/PRs

Close #5949

What changes are proposed in this pull request?

Regenerate protos to fix the following error caused by a recent protobuf release:

ImportError while loading conftest '/home/runner/work/mlflow/mlflow/tests/conftest.py'.
tests/__init__.py:1: in <module>
    from mlflow.utils.logging_utils import _configure_mlflow_loggers
mlflow/__init__.py:32: in <module>
    import mlflow.tracking._model_registry.fluent
mlflow/tracking/__init__.py:8: in <module>
    from mlflow.tracking.client import MlflowClient
mlflow/tracking/client.py:[16](https://github.com/mlflow/mlflow/runs/6605588789?check_suite_focus=true#step:12:17): in <module>
    from mlflow.entities import Experiment, Run, RunInfo, Param, Metric, RunTag, FileInfo, ViewType
mlflow/entities/__init__.py:6: in <module>
    from mlflow.entities.experiment import Experiment
mlflow/entities/experiment.py:2: in <module>
    from mlflow.entities.experiment_tag import ExperimentTag
mlflow/entities/experiment_tag.py:2: in <module>
    from mlflow.protos.service_pb2 import ExperimentTag as ProtoExperimentTag
mlflow/protos/service_pb2.py:[18](https://github.com/mlflow/mlflow/runs/6605588789?check_suite_focus=true#step:12:19): in <module>
    from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2
mlflow/protos/scalapb/scalapb_pb2.py:35: in <module>
    serialized_options=None, file=DESCRIPTOR)
/opt/hostedtoolcache/Python/3.7.12/x64/lib/python3.7/site-packages/google/protobuf/descriptor.py:560: in __new__
    _message.Message._CheckCalledFromGeneratedFile()
E   TypeError: Descriptors cannot not be created directly.
E   If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.[19](https://github.com/mlflow/mlflow/runs/6605588789?check_suite_focus=true#step:12:20).0.
E   If you cannot immediately regenerate your protos, some other possible workarounds are:
E    1. Downgrade the protobuf package to 3.[20](https://github.com/mlflow/mlflow/runs/6605588789?check_suite_focus=true#step:12:21).x or lower.
E    2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).
E
E   More information: https://developers.google.com/protocol-buffers/docs/news/20[22](https://github.com/mlflow/mlflow/runs/6605588789?check_suite_focus=true#step:12:23)-05-06#python-updates

How is this patch tested?

Existing tests

Does this PR change the documentation?

• No. You can skip the rest of this section.
• Yes. Make sure the changed pages / sections render correctly by following the steps below.

1. Check the status of the ci/circleci: build_doc check. If it's successful, proceed to the
   next step, otherwise fix it.
2. Click Details on the right to open the job page of CircleCI.
3. Click the Artifacts tab.
4. Click docs/build/html/index.html.
5. Find the changed pages / sections and make sure they render correctly.

Release Notes

Is this a user-facing change?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release notes for MLflow users.

(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)

What component(s), interfaces, languages, and integrations does this PR affect?

Components

• area/artifacts: Artifact stores and artifact logging
• area/build: Build and test infrastructure for MLflow
• area/docs: MLflow documentation pages
• area/examples: Example code
• area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• area/models: MLmodel format, model serialization/deserialization, flavors
• area/projects: MLproject format, project running backends
• area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• area/server-infra: MLflow Tracking server backend
• area/tracking: Tracking Service, tracking client APIs, autologging

Interface

• area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• area/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
• area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
• area/windows: Windows support

Language

• language/r: R APIs and clients
• language/java: Java APIs and clients
• language/new: Proposals for new client languages

Integrations

• integrations/azure: Azure and Azure ML integrations
• integrations/sagemaker: SageMaker integrations
• integrations/databricks: Databricks integrations

How should the PR be classified in the release notes? Choose one:

• rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
• rn/feature - A new user-facing feature worth mentioning in the release notes
• rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
• rn/documentation - A user-facing documentation change worth mentioning in the release notes

[dbczumar]

LGTM! Thanks @harupy !