Call load_context() when enforcing ChatModel output

[daniellok-db]

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Related Issues/PRs

#xxx

What changes are proposed in this pull request?

I discovered a bug while working on a notebook for ChatModel. If the user has some logic in predict() that depends on load_model() (e.g. setting something like self.model), then prediction will fail. This is actually a problem in normal pyfuncs as well if they're logged with an input example, however in normal pyfuncs we don't enforce outputs, so we just catch the exception and tell the user to save the signature manually.

This PR simply calls load_context() with the artifacts that the user saves before performing predict. I would imagine that most users will need to load some context if they're using ChatModel, so this should unblock a lot of people.

How is this PR tested?

• [ ] Existing unit/integration tests
• [x] New unit/integration tests
• [x] Manual tests

Does this PR require documentation update?

• [x] No. You can skip the rest of this section.
• [ ] Yes. I've updated:
  • [ ] Examples
  • [ ] API references
  • [ ] Instructions

Release Notes

Is this a user-facing change?

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• [ ] Yes. Give a description of this change to be included in the release notes for MLflow users.

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/deployments: MLflow Deployments client APIs, server, and third-party Deployments integrations
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• [ ] area/examples: Example code
• [ ] area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• [x] area/models: MLmodel format, model serialization/deserialization, flavors
• [ ] area/recipes: Recipes, Recipe APIs, Recipe configs, Recipe Templates
• [ ] 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

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• [ ] 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

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• [ ] 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/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/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• [ ] rn/feature - A new user-facing feature worth mentioning in the release notes
• [x] 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

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