[performance] Micro-optimization in '_traceback_filter'

Should be around 40% faster according to this simplified small benchmark:

python -m timeit "a=[0, 1, 2, 3, 4];b=list((e if i in {0, len(a) -1} else str(e)) for i, e in enumerate(a))"
200000 loops, best of 5: 1.12 usec per loop
python -m timeit "a=[0, 1, 2, 3, 4];b=list((a[0], *(str(e) for e in a[1:-1]), a[-1]))"
500000 loops, best of 5: 651 nsec per loop

python -m timeit "a=[0, 1, 2, 3, 4,5,6,7,8,9,10,11,12,13,14,15,16];b=list((e if i in {0, len(a) -1} else str(e)) for i, e in enumerate(a))"
100000 loops, best of 5: 3.31 usec per loop
python -m timeit "a=[0, 1, 2, 3, 4,5,6,7,8,9,10,11,12,13,14,15,16];b=list((a[0], *(str(e) for e in a[1:-1]), a[-1]))"
200000 loops, best of 5: 1.72 usec per loop

src/_pytest/python.py

@@ -1786,11 +1786,10 @@ class Function(PyobjMixin, nodes.Item):
                 if len(ntraceback) > 2:
                     ntraceback = Traceback(
                         (
-                            entry
-                            if i in {0, len(ntraceback) - 1}
-                            else entry.with_repr_style("short")
+                            ntraceback[0],
+                            *(t.with_repr_style("short") for t in ntraceback[1:-1]),
+                            ntraceback[-1],
                         )
-                        for i, entry in enumerate(ntraceback)
                     )
 
             return ntraceback