*   `tf.nn.embedding_lookup_sparse`

    *   Optimized this function for some cases by fusing internal operations.

*   `tf.saved_model.SaveOptions`

copy(em[k-len(prefix)-len(hashed):], prefix) copy(em[k-len(hashed):], hashed) return em, nil } // VerifyPKCS1v15 verifies an RSASSA-PKCS1-v1.5 signature. // // hash is the name of the hash function as returned by [crypto.Hash.String] // or the empty string to indicate that the message is signed directly. func VerifyPKCS1v15(pub *PublicKey, hash string, hashed []byte, sig []byte) error { fipsSelfTest() fips140.RecordApproved() checkApprovedHashNam(hash) return verifyPKCS1v15(pub, hash, hashed, sig) } func verifyPKCS1v15(pub...

FE48          ; disallowed_STD3_mapped ; 005D          # 4.0  PRESENTATION FORM FOR VERTICAL RIGHT SQUARE BRACKET
FE49..FE4C    ; disallowed_STD3_mapped ; 0020 0305     # 1.1  DASHED OVERLINE..DOUBLE WAVY OVERLINE
FE4D..FE4F    ; disallowed_STD3_mapped ; 005F          # 1.1  DASHED LOW LINE..WAVY LOW LINE
FE50          ; disallowed_STD3_mapped ; 002C          # 1.1  SMALL COMMA

* Improved **correctness** in corner cases.
* **Safer** types.
* Better **performance** and **less energy** consumption.
* Better **extensibility**.
* etc.

...all this while keeping the **same Python API**. In most of the cases, for simple models, you can simply upgrade the Pydantic version and get all the benefits. 🚀