[Cleanup] Combine Batched and Regular KMeans Impl

[tarang-jain]

Combine batched and regular k-means implementations

• Unified the batched (host-data) and regular (device-data) k-means fit into a single kmeans_fit template that works with both host and device mdspans via batch_load_iterator
• Unified the device and host initialization paths in init_centroids — both now use raft::matrix::sample_rows which handles host/device transparently
• Removed the inertia_check parameter — inertia-based convergence checking now always runs. Zero clustering cost (perfect fit) logs a warning instead of asserting. This is needed because spectral clustering can cause all points to converge on the cluster centroids itself.
• Added init_size parameter to control how many samples are drawn for KMeansPlusPlus initialization. Defaults to n_samples for device data, min(3 * n_clusters, n_samples) for host data
• Replaced per-iteration centroid raft::copy with std::swap of buffer pointers
• For streaming fit, precompute data norms once and cache them: host norms cached to a host buffer on the first iteration and copied back for subsequent iterations. process_batch no longer computes norms internally
• Replaced raw cudaPointerGetAttributes call with raft::memory_type_from_pointer
• Updated compute_weight_scale to use raft handle and mdspan-based raft::copy
• Precompute centroid norms once per Lloyd iteration and pass to minClusterAndDistanceCompute via a new optional precomputed_centroid_norms parameter, avoiding redundant recomputation across batches

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