*   Introduce the `tf.compat.v1.keras.utils.track_tf1_style_variables`
    decorator, which enables using large classes of tf1-style variable_scope,
    `get_variable`, and `compat.v1.layer`-based components from within TF2
    models running with TF2 behavior enabled.

*   `tf.data`:

    *   tf.data service now supports auto-sharding. Users specify the sharding

tmp2.FromP1xP1(tmp1) // tmp2 = 4*v in P2 coords tmp1.Double(tmp2) // tmp1 = 8*v in P1xP1 coords tmp2.FromP1xP1(tmp1) // tmp2 = 8*v in P2 coords tmp1.Double(tmp2) // tmp1 = 16*v in P1xP1 coords v.fromP1xP1(tmp1) // now v = 16*(odd components) // Accumulate the even components for i := 0; i < 64; i += 2 { basepointTable[i/2].SelectInto(multiple, digits[i]) tmp1.AddAffine(v, multiple) v.fromP1xP1(tmp1) } return v } // ScalarMult sets v = x * q, and returns v. // // The scalar multiplication is done in...

tmp2.FromP1xP1(tmp1) // tmp2 = 4*v in P2 coords tmp1.Double(tmp2) // tmp1 = 8*v in P1xP1 coords tmp2.FromP1xP1(tmp1) // tmp2 = 8*v in P2 coords tmp1.Double(tmp2) // tmp1 = 16*v in P1xP1 coords v.fromP1xP1(tmp1) // now v = 16*(odd components) // Accumulate the even components for i := 0; i < 64; i += 2 { basepointTable[i/2].SelectInto(multiple, digits[i]) tmp1.AddAffine(v, multiple) v.fromP1xP1(tmp1) } return v } // ScalarMult sets v = x * q, and returns v. // // The scalar multiplication is done in...