mFour) if err != nil { t.Fatal(err) } checkYes(n.IsMinusOne(mFour), "3 is not -1 mod 4") checkNot(n.IsZero(), "3 is zero") checkNot(n.IsOne(), "3 is one") checkYes(n.IsOdd(), "3 is not odd") n.SubOne(mFour) checkNot(n.IsMinusOne(mFour), "2 is -1 mod 4") checkNot(n.IsZero(), "2 is zero") checkNot(n.IsOne(), "2 is one") checkNot(n.IsOdd(), "2 is odd") n.SubOne(mFour) checkNot(n.IsMinusOne(mFour), "1 is -1 mod 4") checkNot(n.IsZero(), "1 is zero") checkYes(n.IsOne(), "1 is not one") checkYes(n.IsOdd(), "1...

        `max_batch_size` for building TensorRT engines. Add parameters
        `use_dynamic_shape` to enable dynamic shape support. The default is to
        disable dynamic shape support. Add `dynamic_shape_profile_strategy` for
        selecting a dynamic shape profile strategy. The default is profile
        strategy is `Range`.
    *   Issue a warning when function get_tensorrt_rewriter_config is used.

`UserInDB` that inherits from `User` but contains extra fields, like `hashed_password`, and `User` is used in the `response_model`), it would still pass the validation (because `UserInDB` is a subclass of `User`) and the object would be returned as-is, including the `hashed_password`. To fix this, the declared `response_model` is cloned, if it is a Pydantic model class (or contains Pydantic model classes in it, e.g. in a `List[Item]`), the Pydantic model class(es) will be a different one (the "cloned"...