feat(trainer): implement get_gpu_status for Kubernetesbackend

[haroon0x]

What this PR does / why we need it:
This PR implements the get_gpu_status method in the KubernetesBackend, allowing users to retrieve real-time GPU metrics from training pods. This feature enhances observability for AI workloads by providing structured data on GPU utilization, memory usage, temperature, power draw, and performance states.

Key technical changes:

• Base Layer: Added get_gpu_status as an abstract method in RuntimeBackend.
• API Layer: Exposed get_gpu_status(job_name) in TrainerClient.
• Kubernetes Implementation: Uses the Kubernetes exec API to run nvidia-smi inside training pods (node-*). It uses a structured CSV query format (--query-gpu=...) for reliable parsing.
• Compatibility: Added placeholder implementations for LocalProcessBackend and ContainerBackend to ensure all backends satisfy the revised interface.
• Verification: Added test_get_gpu_status to kubeflow/trainer/backends/kubernetes/backend_test.py which mocks the Kubernetes stream response.

Which issue(s) this PR fixes (optional, in Fixes #<issue number>, #<issue number>, ... format, will close the issue(s) when PR gets merged):

Fixes #165

Checklist:

• Docs included if any changes are user facing
• Unit tests added/updated.
• All 50 existing tests passed (Kubernetes, Local, Container backends).

[google-oss-prow]

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