[BUG] Achieve ROCKET GPU kernel and feature parity using CPU kernel generation

[Adityakushwaha2006]

Reference Issues/PRs

Related to #1248

What does this implement/fix? Explain your changes.

This PR implements kernel and feature parity between CPU and GPU ROCKET implementations by reusing the CPU's kernel generation function while maintaining GPU acceleration for transform operations.

Changes:

• Import CPU's kernel generation function for identical kernel parameters
• Add conversion method to transform sparse channel indexing to dense format compatible with TensorFlow convolutions
• Update CPU-GPU parity test to use decimal=4 threshold (previously xfail, now passes)
• Removed GPU-specific kernel generation parameters as they're now derived from CPU logic

Results:

• Kernel parity: 100% exact match (identical weights, biases, dilations, channel selections)
• Feature parity: Features match within 1e-4 (0.0001) precision (most datasets show even higher precision of the order 1e-5 or 1e-7)
• Tested on: Both univariate and multivariate datasets.

Key insight:
The sparse to dense conversion places CPU's selected channel weights at correct positions in a dense kernel, with zeros for non selected channels. Since zero weights contribute nothing to convolution, this achieves mathematical equivalence while using standard TensorFlow operations.

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

No.

Any other comments?

None.

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