Bugfix: properly pass kwargs to torch.jit.load from mlfow.pytorch.load_model

[schmidt-jake]

What changes are proposed in this pull request?

Properly dispatch kwargs provided in mlflow.pytorch.load_model to torch.jit.load. In certain cases, it's crucial that we are able to set the map_location kwarg in torch.jit.load for proper model function. Currently, this is not supported in mlflow.pytorch.load_model due to a small oversight in an internal function.

How is this patch tested?

Unsure, need GPU test environment to check e.g.map_location is being called correctly.

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

^{Created from VS Code using CodeStream}

[github-actions]

@schmidt-jake Thanks for the contribution! The DCO check failed. Please sign off your commits by following the instructions here: https://github.com/mlflow/mlflow/runs/4514699072. See https://github.com/mlflow/mlflow/blob/master/CONTRIBUTING.rst#sign-your-work for more details.

[harupy]

@schmidt-jake Thanks for the catch, LGTM! Could you fix the DCO check failure by following the instructions in the comment above?

[schmidt-jake]

@harupy Done!