blockloop: VLD1.P 16(R1), [V4.B16] // load 16bytes message VLD1.P 16(R1), [V5.B16] // load 16bytes message VLD1.P 16(R1), [V6.B16] // load 16bytes message VLD1.P 16(R1), [V7.B16] // load 16bytes message VMOV V0.B16, V2.B16 // backup: VO h(dcba) VMOV V1.B16, V3.B16 // backup: V1 h(hgfe) VMOV V2.B16, V8.B16 VREV32 V4.B16, V4.B16 // prepare for using message in Byte format VREV32 V5.B16, V5.B16 VREV32 V6.B16, V6.B16 VREV32 V7.B16, V7.B16 VADD V16.S4, V4.S4, V9.S4 // V18(W0+K0...W3+K3) SHA256SU0 V5.S4,...

    *   XLA reduction emitter is deterministic when the environment variable
        `TF_DETERMINISTIC_OPS` is set to "true" or "1". This extends
        deterministic `tf.nn.bias_add` back-prop functionality (and therefore
        also deterministic back-prop of bias-addition in Keras layers) to
        include when XLA JIT compilation is enabled.
    *   Fix problem, when running on a CUDA GPU and when either environment