Same situation as: https://bugs.python.org/issue39425

Mean +- std dev: [master_count] 946 ns +- 14 ns -> [ac_count] 427 ns +- 7 ns: 2.22x faster (-55%)

Benchmark file

I would rather not do this.   It optimizes for the uncommon case where all the objects are identical.  The common case is slightly worse off because the identity test is performed twice, once before the call to Py_RichCompareBool() and again inside it.  Also, the PR adds a little clutter which obscures the business logic.  

Another thought, micro-benchmarks on the identity tests require some extra care because they are super sensitive to branch prediction failures (See https://stackoverflow.com/questions/11227809 ).  A more realistic dataset would be:

  x = 12345
  data = [x] * 100 + list(range(500))
  random.shuffle(data)
  data.count(x)

> I would rather not do this.

I would not like to say this change should be applied ;)
I found this point during I converting deque methods by using 
Argument Clinic(I will ping you later ;)


https://bugs.python.org/issue39425 was applied since PyObject_RichCompareBool requires reference counting and it caused performance regression. 

Know I remember, why we applied this micro-optimization.

So my conclusion is that don't apply this change.
We don't have to apply this change since PyObject_RichCompareBool does not cause a performance regression.

Thank you for your comment, Raymond.