RFE: Add batching support (N-D tensors) to the gemm operator

[mtavenrath]

Problem Statement
Currently, the MLGraphBuilder.gemm() operation is restricted to 2D inputs for the a and b operands. However, MLGraphBuilder.matmul() fully supports batching via N-dimensional tensors.

If a developer wants to perform a batched General Matrix Multiplication — calculating $\alpha A B + \beta C$ across a batch — they cannot use the gemm operator natively.

Current Workaround & Limitations
To achieve a batched GEMM, developers currently have to manually emulate the operation by composing a subgraph of multiple operators:

1. matmul (to handle the batched multiplication of A and B)
2. mul (to scale the result by $\alpha$)
3. mul (to scale C by $\beta$)
4. add (to combine the results)

This emulation is undesirable for a few reasons:

• Graph Bloat: It increases the number of nodes in the graph, adding overhead to graph construction and compilation.
• Performance Penalties: While some backend compilers might successfully fuse these separate matmul, mul, and add nodes back into a single batched GEMM operation, this fusion is not guaranteed. If the fusion step fails or isn't supported by a specific hardware backend, the execution will incur multiple memory read/write passes instead of utilizing a single, highly optimized hardware kernel (e.g., batched GEMM on GPUs or NPUs).

Proposed Solution
Update the gemm operator specification to accept N-dimensional tensors (where rank >= 2). For inputs of rank > 2, the leading dimensions should be treated as batch dimensions exactly like the matmul operation currently does.