input subscripts. Furthermore, all dimensions mapping to the same axis label
must be equal.

Any of the input and output subscripts may contain at most a single ellipsis
(`...`). These ellipsis are mapped against dimensions not corresponding to any
named axis label. If two inputs contain ellipsis, then they are broadcasted
according to standard NumPy broadcasting
[rules](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html).

    *   Delete unused `Fingerprint64Map` op registration
    *   Add broadcasting support to `tf.matmul`.
    *   Add C++ Gradient for `BatchMatMulV2`.
    *   Add `tf.math.cumulative_logsumexp` operation.
    *   Add ellipsis (...) support for `tf.einsum()`.
    *   Add expand_composites argument to all `nest.*` methods.
    *   Added `strings.byte_split`.
    *   Add a new "result_type" parameter to `tf.strings.split`.