[ENH] Add GPU support to RocketClassifier/Regressor

[Adityakushwaha2006]

Reference Issues/PRs

Fixes #1542
Already supports the backend change (CuPy based ) brought about in #3211

What does this implement/fix? Explain your changes.

This PR implements the GPU integration interface for RocketClassifier and RocketRegressor. It introduces a use_gpu=True parameter that seamlessly switches the underlying transformer to the GPU implementation when enabled.

Key Implementation Details:

• Zero-Config GPU: Users simply pass use_gpu=True.
• Soft Dependency Handling: Uses _check_soft_dependencies("tensorflow") to ensure a clean error message if the user enables GPU mode without the backend installed.
• Backend Decoupled: The implementation uses a dynamic import pattern. This ensures the Classifier/Regressor logic remains backend-agnostic (allowing for future swaps to CuPy/Torch without breaking the API).
• Backward Compatibility: Default behavior (use_gpu=False) is strictly preserved and uses the standard CPU transformer.

Example Usage:

# Standard CPU usage (Unchanged)
clf = RocketClassifier(n_kernels=500)

# New GPU usage (Requires TensorFlow backend currently)
clf_gpu = RocketClassifier(n_kernels=500, use_gpu=True)
# clf_gpu.fit(X_train, y_train)

Does your contribution introduce a new dependency? If yes, which one?

No new mandatory dependencies. It adds tensorflow as a soft dependency (optional). The code only attempts to import it if use_gpu=True is explicitly set.

Any other comments?

Context: I am picking this up following my check-in on Issue #1542 (assignee inactive since Aug 2024) to unblock the GPU integration roadmap requested by @hadifawaz1999.

Verification: Verified locally with the following matrix:

• use_gpu=False (Default): Matches CPU baseline exactly.
• use_gpu=True: Successfully initializes GPU transformer pipeline.

Missing Dependency Check: Verified that setting use_gpu=True without TensorFlow raises a clear ImportError rather than a crash.

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I have added the following labels to this PR based on the title: [ enhancement ].
I have added the following labels to this PR based on the changes made: [ classification, regression ]. Feel free to change these if they do not properly represent the PR.

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