[Quant] flip: throw runtime error for QUInt4x2 and QUInt2x4 input (#147430)

Fixes #147208

**Summary**
The `flip` op causes memory corruption for `torch.quint4x2` and `torch.quint2x4` inputs. It is because the TensorIterator-based implementation does not support multiple elements per byte. And `torch.quint4x2` and `torch.quint2x4` are deprecated in PyTorch. So, we add a check here to throw a runtime error if input dtyps is `torch.quint4x2` or `torch.quint2x4`.

**Test plan**
```
pytest -s test/test_shape_ops.py -k test_flip_unsupported_dtype
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/147430
Approved by: https://github.com/mingfeima, https://github.com/ngimel

aten/src/ATen/native/TensorTransformations.cpp

@@ -34,6 +34,11 @@
 namespace at::native {
 
 Tensor flip(const Tensor& self, IntArrayRef dims) {
+  TORCH_CHECK(
+      self.scalar_type() != at::kQUInt4x2 &&
+          self.scalar_type() != at::kQUInt2x4,
+      "flip is not supported for tensor with data type ",
+      self.scalar_type());
   const int64_t total_dims = self.dim();
   // It wraps the dims and checks that there are no repeated dims
   auto flip_dims_b = at::dim_list_to_bitset(dims, total_dims);

test/test_shape_ops.py

@@ -581,6 +581,16 @@ class TestShapeOps(TestCase):
         self.compare_with_numpy(torch_fn, np_fn, t_in)
         del t_in
 
+    @onlyCPU
+    @unittest.expectedFailure
+    @dtypes(torch.quint4x2, torch.quint2x4)
+    def test_flip_unsupported_dtype(self, dtype):
+        scale, zero_point = 0.1, 5
+        qt = torch.quantize_per_tensor(
+            torch.randn(16, 16), scale=scale, zero_point=zero_point, dtype=dtype
+        )
+        torch.flip(qt, dims=(0,))
+
     def _test_fliplr_flipud(self, torch_fn, np_fn, min_dim, max_dim, device, dtype):
         for dim in range(min_dim, max_dim + 1):
             shape = self._rand_shape(dim, 5, 10)