#17871: skip mixed dtype case for ttnn.experimental.pow (#17884)

### Ticket
Link to Github Issue #17871

### Problem description
A specific combination of input datatype in
`tests/ttnn/unit_tests/operations/eltwise/test_pow.py::test_binary_sfpu_pow_bug
` fails once or twice when run in a loop
`pytest --count=20
tests/ttnn/unit_tests/operations/eltwise/test_pow.py::test_binary_sfpu_pow_bug`


### What's changed
Skipping the test case until we debug 
Tracked in #17883

### Checklist
- [x] [All post
commit](https://github.com/tenstorrent/tt-metal/actions/workflows/all-post-commit-workflows.yaml)
CI passes
https://github.com/tenstorrent/tt-metal/actions/runs/13365555065
- [ ] [Blackhole Post
commit](https://github.com/tenstorrent/tt-metal/actions/workflows/blackhole-post-commit.yaml)
CI passes (if applicable)
- [ ] [Model
regression](https://github.com/tenstorrent/tt-metal/actions/workflows/perf-models.yaml)
CI passes (if applicable)
- [ ] [Device performance
regression](https://github.com/tenstorrent/tt-metal/actions/workflows/perf-device-models.yaml)
CI passes (if applicable)
- [ ] **(For models and ops writers)** Full [new models
tests](https://github.com/tenstorrent/tt-metal/actions/workflows/full-new-models-suite.yaml)
CI passes (if applicable)
- [ ] New/Existing tests provide coverage for changes

tests/ttnn/unit_tests/operations/eltwise/test_pow.py

@@ -263,8 +263,8 @@ def test_binary_pow(device, dtype_a, dtype_b, ttnn_function):
     ],
 )
 def test_binary_sfpu_pow_bug(device, input_shapes, dtype_a, dtype_b, ttnn_function):
-    if (ttnn_function == ttnn.pow) and (dtype_a != dtype_b):
-        pytest.skip("Mixed datatypes not supported in ttnn.pow")
+    if dtype_a != dtype_b:
+        pytest.skip("Mixed datatypes not supported in ttnn.pow or ttnn.experimental.pow")
     torch.manual_seed(0)
     torch_dtype_a = getattr(torch, dtype_a)
     ttnn_dtype_a = getattr(ttnn, dtype_a)

ttnn/cpp/ttnn/operations/eltwise/binary/binary_pybind.hpp

@@ -1825,7 +1825,9 @@ void py_module(py::module& module) {
         R"doc(BFLOAT16, BFLOAT8_B)doc");
 
     detail::bind_power(
-        module, ttnn::pow, R"doc(When :attr:`exponent` is a Tensor, supported dtypes are: BFLOAT16, FLOAT32)doc");
+        module,
+        ttnn::pow,
+        R"doc(When :attr:`exponent` is a Tensor, supported dtypes are: BFLOAT16, FLOAT32. Both input tensors should be of same dtype.)doc");
 }
 
 }  // namespace binary