Docs(gpu): Update README to reflect current script capabilities

This commit comprehensively updates gpu/README.md to align with the
current features, metadata, and behavior of install_gpu_driver.sh.

Key updates to the README include:

- Default Versions & Configurations:
  - Clarified that the script's internal version matrix is based on
    NVIDIA's guidance (e.g., Deep Learning Frameworks Support Matrix).
  - Updated example default CUDA versions for different Dataproc
    image series (2.0, 2.1, 2.2+).
  - Expanded the table of "Example Tested Configurations" with more
    recent and relevant CUDA/Driver/cuDNN/NCCL versions and their
    tested Dataproc image compatibility.
  - Updated the list of "Supported Operating Systems."

- Usage Examples:
  - Revised `gcloud` examples for clarity, using current best practices
    (regionalized bucket paths, common GPU types).
  - Added a new example demonstrating the use of `cuda-url` and
    `gpu-driver-url` with HTTP/HTTPS URLs.
  - Updated the MIG (Multi-Instance GPU) example to correctly show
    `install_gpu_driver.sh` for base drivers and `mig.sh` (via
    `dataproc:startup.script.uri`) for MIG-specific setup.

- Custom Image Creation:
  - Clarified the use of `invocation-type=custom-images` metadata,
    emphasizing it's set by image building tools (like
    `generate_custom_image.py`) and not by end-users creating
    clusters from scratch.
  - Provided a simplified example for `generate_custom_image.py`.

- Feature Documentation:
  - Updated "GPU Scheduling in YARN" to reflect current configurations,
    including the RAPIDS Spark plugin.
  - Revised the "cuDNN" section for clarity on version selection and
    installation methods.
  - Significantly expanded "Loading Built Kernel Module & Secure Boot"
    with details on MOK key management via GCP Secret Manager, the role
    of `GoogleCloudDataproc/custom-images/examples/secure-boot/create-key-pair.sh`,
    and the `--no-shielded-secure-boot` workaround.

- Metadata Parameters:
  - Ensured the list is comprehensive and descriptions are accurate for
    all current parameters, including:
    - `cuda-url` and `gpu-driver-url` (clarifying they expect HTTP/HTTPS
      URLs for `curl` fetching).
    - `include-pytorch` and `gpu-conda-env`.
    - `container-runtime`.
    - Full set of Secure Boot signing parameters.
  - Corrected default values where necessary (e.g., `install-gpu-agent`
    is now `true` by default).

- Verification, Reporting, and Troubleshooting:
  - Updated verification commands.
  - Clarified that the "Report Metrics" section now refers to the
    automated agent (based on ml-on-gcp code) and that
    `create_gpu_metrics.py` is no longer used by this init action.
  - Revised troubleshooting tips to be more relevant to current issues.

- Important Notes:
  - Added detailed "Performance & Caching" subsection explaining:
    - The GCS caching mechanism (`dataproc-temp-bucket`).
    - Potential long first-run times (up to 150 mins on small nodes) if
      compiling from source.
    - The recommendation and benefits of "pre-warming" the cache on a
      larger instance (reducing init action time to ~12-20 mins in some
      cases).
    - The security benefit of reduced attack surface when using cached
      artifacts (as build tools may not be needed).
  - Updated notes on SSHD hardening and APT source management.
  - Confirmed primary support for Dataproc 2.0+ images.

- Formatting and Style:
  - Maintained the overall structure and line-wrapping style (aiming
    for ~80 columns) of the provided baseline README (md5sum
    2daece9a7841cc4f5a0997fecf68cbd7) where feasible, while ensuring
    clarity and readability of the new and updated content.

Author: cjac