[AI Manager] Add az aimanager model commands - #10217

🤖 PR Validation — ⚠️ Review suggested

Add the new az aimanager model command group, mapping to the AIModels operations of the Microsoft.ContainerService aiModels resource at api-version 2026-05-02-preview.

calculate-cost posts an empty request body to /subscriptions/{subscriptionId}/providers/Microsoft.ContainerService/locations/{location}/aiModels/{modelName}/calculateCost and returns the ranked list of GPU SKU pricing plans (feasibility, per-replica hourly cost, max available replicas, estimated relative performance). No Azure or Kubernetes resources are provisioned.

AI models are read-only, region-scoped catalog entries, so this group only exposes read/action commands - there is no create/update/delete, no --resource-group, and no --no-wait. az aimanager model list is the intended way to discover the opaque model name used by --model-resource-id on az aimanager namespace modeldeployment add.

Table transformers are included for list and calculate-cost:

$ az aimanager model calculate-cost -l eastus2 -n 9806f0c862fdd920 -o table
VmSize                    Feasible    VmsPerReplica    VmHourlyPrice    TotalHourlyPrice    MaxAvailableReplicas    Quantization
------------------------  ----------  ---------------  ---------------  ------------------  ----------------------  --------------
Standard_NC24ads_A100_v4  True        1                3.67             3.67                4                       fp8

The vendored SDK already contained AIModelsOperations and the CalculateCostRequest/CalculateCostResponse models, so no SDK regeneration was needed. Extension version bumped 1.2.0 -> 1.2.1b1 with a matching HISTORY.rst entry.

Testing

• New test_aimodel.py unit tests covering show/list/calculate-cost plumbing (including that calculate-cost sends an empty CalculateCostRequest body) and the new name validator.
• New test_aimodel_scenario.py mocked scenario test exercising all three commands end-to-end through the CLI parser, following the existing test_modeldeployment_scenario.py pattern.
• azdev style (flake8) clean and pylint 10.00/10 against the repo configs; existing aimanager tests unaffected.

This checklist is used to make sure that common guidelines for a pull request are followed.

Related command

az aimanager model show | list | calculate-cost

General Guidelines

• Have you run azdev style <YOUR_EXT> locally? (pip install azdev required)
• Have you run python scripts/ci/test_index.py -q locally? (pip install azdev required)
• My extension version conforms to the Extension version schema

About Extension Publish

There is a pipeline to automatically build, upload and publish extension wheels.
Once your pull request is merged into main branch, a new pull request will be created to update src/index.json automatically.
You only need to update the version information in file setup.py and historical information in file HISTORY.rst in your PR but do not modify src/index.json.