oat-none {\n  float: none !important;\n}\n\n.object-fit-contain {\n  -o-object-fit: contain !important;\n  object-fit: contain !important;\n}\n\n.object-fit-cover {\n  -o-object-fit: cover !important;\n  object-fit: cover !important;\n}\n\n.object-fit-fill {\n  -o-object-fit: fill !important;\n  object-fit: fill !important;\n}\n\n.object-fit-scale {\n  -o-object-fit: scale-down !important;\n  object-fit: scale-down !important;\n}\n\n.object-fit-none {\n  -o-object-fit: none !important;\n  object-fit:...

*   `tf.keras`:

    *   `Model.fit` major improvements:
        *   You can now use custom training logic with `Model.fit` by overriding
            `Model.train_step`.
        *   Easily write state-of-the-art training loops without worrying about
            all of the features `Model.fit` handles for you (distribution
            strategies, callbacks, data formats, looping logic, etc)

t.Errorf("%d: got %v, expected %v", i, got, exp) } } } func TestModulusAndNatSiz(t *testing.T) { // These are 126 bit (2 * _W on 64-bit architectures) values, serialized as // 128 bits worth of bytes. If leading zeroes are stripped, they fit in two // limbs, if they are not, they fit in three. This can be a problem because // modulus strips leading zeroes and nat does not. m := modulusFromBytes([]byte{ 0x3f, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff}) xb := []byte{0x3f,...

t.Errorf("%d: got %v, expected %v", i, got, exp) } } } func TestModulusAndNatSiz(t *testing.T) { // These are 126 bit (2 * _W on 64-bit architectures) values, serialized as // 128 bits worth of bytes. If leading zeroes are stripped, they fit in two // limbs, if they are not, they fit in three. This can be a problem because // modulus strips leading zeroes and nat does not. m := modulusFromBytes([]byte{ 0x3f, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff}) xb := []byte{0x3f,...