var i = 1<<s                   // 1 has type int
var j int32 = 1<<s             // 1 has type int32; j == 0
var k = uint64(1<<s)           // 1 has type uint64; k == 1<<33
var m int = 1.0<<s             // 1.0 has type int; m == 1<<33
var n = 1.0<<s == j            // 1.0 has type int32; n == true
var o = 1<<s == 2<<s           // 1 and 2 have type int; o == false

        tf.TensorSpec([], dtypes.int32) ]) def _remote_multiply(a, b): return
        tf.math.multiply(a, b)

        server.register("multiply", _remote_multiply) ```

    *   Example usage to create client: `python client =
        tf.distribute.experimental.rpc.Client.create("grpc", address) a =
        tf.constant(2, dtype=tf.int32) b = tf.constant(3, dtype=tf.int32)