The new optimizer's `learning_rate` property returns the
        current learning rate value instead of a `LearningRateSchedule` object
        as before. If you need to access the `LearningRateSchedule` object,
        please use `optimizer._learning_rate`.
    *   **You implemented a custom optimizer based on the old optimizer.**
        Please set your optimizer to subclass

classes in it, e.g. in a `List[Item]`), the Pydantic model class(es) will be a different one (the "cloned" one). So, an object that is a subclass won't simply pass the validation and returned as-is, because it is no longer a sub-class of the cloned `response_model`. Instead, a new Pydantic model object will be created with the contents of the returned object. So, it will be a new object (made with the data from the returned one), and will be filtered by the cloned `response_model`, containing only the...

organization(1) gov(101) csor(3) nistAlgorithm(4) } // // hashAlgs OBJECT IDENTIFIER ::= { nistAlgorithms 2 } // // id-sha256 OBJECT IDENTIFIER ::= { hashAlgs 1 } // id-sha384 OBJECT IDENTIFIER ::= { hashAlgs 2 } // id-sha512 OBJECT IDENTIFIER ::= { hashAlgs 3 } // id-sha224 OBJECT IDENTIFIER ::= { hashAlgs 4 } // id-sha512-224 OBJECT IDENTIFIER ::= { hashAlgs 5 } // id-sha512-256 OBJECT IDENTIFIER ::= { hashAlgs 6 } // id-sha3-224 OBJECT IDENTIFIER ::= { hashAlgs 7 } // id-sha3-256 OBJECT IDENTIFIER...

organization(1) gov(101) csor(3) nistAlgorithm(4) } // // hashAlgs OBJECT IDENTIFIER ::= { nistAlgorithms 2 } // // id-sha256 OBJECT IDENTIFIER ::= { hashAlgs 1 } // id-sha384 OBJECT IDENTIFIER ::= { hashAlgs 2 } // id-sha512 OBJECT IDENTIFIER ::= { hashAlgs 3 } // id-sha224 OBJECT IDENTIFIER ::= { hashAlgs 4 } // id-sha512-224 OBJECT IDENTIFIER ::= { hashAlgs 5 } // id-sha512-256 OBJECT IDENTIFIER ::= { hashAlgs 6 } // id-sha3-224 OBJECT IDENTIFIER ::= { hashAlgs 7 } // id-sha3-256 OBJECT IDENTIFIER...