*   Add HW acceleration support for `topK_v2`.
*   Add new `TypeSpec` classes.
*   CloudBigtable version updated to v0.10.0.
*   Expose `Head` as public API.
*   Update docstring for gather to properly describe the non-empty `batch_dims`
    case.
*   Added `tf.sparse.from_dense` utility function.
*   Improved ragged tensor support in `TensorFlowTestCase`.

* In `encoders.jsonable_encoder` remove parameter `sqlalchemy_safe`.
    * It was an early hack to allow returning SQLAlchemy models, but it was never documented, and the recommended way is using Pydantic's `orm_mode` as described in the tutorial: [SQL (Relational) Databases](https://fastapi.tiangolo.com/tutorial/sql-databases/).
    * PR [#1864](https://github.com/tiangolo/fastapi/pull/1864).

### Docs

b.limbs[:n] switch n { default: // Attempt to use a stack-allocated backing array. T := make([]uint, 0, preallocLimbs*2) if cap(T) < n*2 { T = make([]uint, 0, n*2) } T = T[:n*2] // This loop implements Word-by-Word Montgomery Multiplication, as // described in Algorithm 4 (Fig. 3) of "Efficient Software // Implementations of Modular Exponentiation" by Shay Gueron // [https://eprint.iacr.org/2011/239.pdf]. var c uint for i := 0; i < n; i++ { _ = T[n+i] // bounds check elimination hint // Step 1 (T =...

b.limbs[:n] switch n { default: // Attempt to use a stack-allocated backing array. T := make([]uint, 0, preallocLimbs*2) if cap(T) < n*2 { T = make([]uint, 0, n*2) } T = T[:n*2] // This loop implements Word-by-Word Montgomery Multiplication, as // described in Algorithm 4 (Fig. 3) of "Efficient Software // Implementations of Modular Exponentiation" by Shay Gueron // [https://eprint.iacr.org/2011/239.pdf]. var c uint for i := 0; i < n; i++ { _ = T[n+i] // bounds check elimination hint // Step 1 (T =...