* New docs at [Dependencies with `yield`](https://fastapi.tiangolo.com/tutorial/dependencies/dependencies-with-yield/).
    * Updated database docs [SQL (Relational) Databases: Main **FastAPI** app](https://fastapi.tiangolo.com/tutorial/sql-databases/#main-fastapi-app).
    * PR [#595](https://github.com/tiangolo/fastapi/pull/595).

subtle import "crypto/internal/fips140/alias" // XORBytes sets dst[i] = x[i] ^ y[i] for all i < n = min(len(x), len(y)), // returning n, the number of bytes written to dst. // // If dst does not have length at least n, // XORBytes panics without writing anything to dst. // // dst and x or y may overlap exactly or not at all, // otherwise XORBytes may panic. func XORBytes(dst, x, y []byte) int { n := min(len(x), len(y)) if n == 0 { return 0 } if n > len(dst) { panic("subtle.XORBytes: dst too short")...

subtle import "crypto/internal/fips140/alias" // XORBytes sets dst[i] = x[i] ^ y[i] for all i < n = min(len(x), len(y)), // returning n, the number of bytes written to dst. // // If dst does not have length at least n, // XORBytes panics without writing anything to dst. // // dst and x or y may overlap exactly or not at all, // otherwise XORBytes may panic. func XORBytes(dst, x, y []byte) int { n := min(len(x), len(y)) if n == 0 { return 0 } if n > len(dst) { panic("subtle.XORBytes: dst too short")...

    of other integer types) in `tf.constant`.
*   Make the `gain` argument of convolutional orthogonal initializers
    (`convolutional_delta_orthogonal`, `convolutional_orthogonal_1D`,
    `convolutional_orthogonal_2D`, `convolutional_orthogonal_3D`) have
    consistent behavior with the `tf.initializers.orthogonal` initializer, i.e.
    scale the output l2-norm by `gain` and NOT by `sqrt(gain)`. (Note that these