Update on "Inductor cpp wrapper: fix dtype of ShapeAsConstantBuffer"

For `at::scalar_tensor` the default dtype will be `float` ([link to scalar_tensor](https://github.com/pytorch/pytorch/blob/0d8e960f74acd359358e0b729c4803d2b71849e5/aten/src/ATen/native/TensorFactories.cpp#L856), [link to default dtype](https://github.com/pytorch/pytorch/blob/0d8e960f74acd359358e0b729c4803d2b71849e5/c10/core/TensorOptions.h#L551)) if we don't set the `dtype` value. However, the input scalar value is not necessarily a `float` value. With `torch::tensor(x)`, the dtype of the tensor will be decided according to the dtype of the scalar.

cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx peterbell10 ipiszy yf225 chenyang78 kadeng muchulee8 aakhundov ColinPeppler amjames desertfire chauhang

[ghstack-poisoned]

test/inductor/test_torchinductor.py

@@ -2469,7 +2469,6 @@ def fn(arg0_1, arg2_1):
             return (full, arg1_1, embedding)
 
         arg0_1 = rand_strided((32128, 768), (768, 1), device="cpu", dtype=torch.float32)
-        arg1_1 = 22
         arg2_1 = rand_strided((1, 22), (22, 1), device="cpu", dtype=torch.int64)
         self.common(fn, [arg0_1, arg2_1])