[Inductor] Fix the decompositions of torch isin (#147519)

**Summary**
Fixed two decomposition issues in `torch.isin`:

- Issue 1: As reported in [#147329](https://github.com/pytorch/pytorch/issues/147329), the current decomposition does not support cases where test_element is a scalar. This is now implemented by referring to the ead970c8d0/aten/src/ATen/native/TensorCompare.cpp (L1004-L1008)

- Issue 2: Found while enabling a unit test with `elements = 1` and `test_elements = torch.tensor([1, 2, 3, 4])`, where Inductor produced different results compared to eager mode. This issue is fixed by referring to ead970c8d0/aten/src/ATen/native/cpu/TensorCompareKernel.cpp (L329-L338)

**Test Plan**
```
python test/inductor/test_torchinductor.py -k test_isin_tensor_scalar
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/147519
Approved by: https://github.com/jgong5, https://github.com/FFFrog, https://github.com/peterbell10

test/inductor/test_torchinductor.py

@@ -12253,6 +12253,17 @@ class CommonTemplate:
 
         self.common(forward, (a, b))
 
+    def test_isin_tensor_scalar(self):
+        for invert in [True, False]:
+            torch._dynamo.reset()
+            elements = 1
+            test_elements = torch.tensor([1, 2, 3, 4])
+            self.common(torch.isin, (elements, test_elements), {"invert": invert})
+            torch._dynamo.reset()
+            elements = torch.tensor([1, 2, 3, 4])
+            test_elements = 1
+            self.common(torch.isin, (elements, test_elements), {"invert": invert})
+
     def test_mul_index_expr(self):
         # Minified repro from https://github.com/pytorch/pytorch/issues/111884
         def forward():

torch/_decomp/decompositions.py

@@ -5091,7 +5091,10 @@ def isin(elements, test_elements, *, assume_unique=False, invert=False):
     if not isinstance(elements, torch.Tensor):
         elements = torch.tensor(elements, device=test_elements.device)
     if not isinstance(test_elements, torch.Tensor):
-        test_elements = torch.tensor(test_elements, device=elements.device)
+        if invert:
+            return torch.ne(elements, test_elements)
+        else:
+            return torch.eq(elements, test_elements)
 
     if test_elements.numel() < 10.0 * pow(elements.numel(), 0.145):
         return isin_default(elements, test_elements, invert=invert)
@@ -5123,14 +5126,10 @@ def bernoulli(
 def isin_default(elements, test_elements, *, invert=False):
     if elements.numel() == 0:
         return torch.empty_like(elements, dtype=torch.bool)
-
     x = elements.view(*elements.shape, *((1,) * test_elements.ndim))
-    if not invert:
-        cmp = x == test_elements
-    else:
-        cmp = x != test_elements
     dim = tuple(range(-1, -test_elements.ndim - 1, -1))
-    return cmp.any(dim=dim)
+    res = (x == test_elements).any(dim=dim)
+    return ~res if invert else res
 
 
 def isin_sorting(elements, test_elements, *, assume_unique=False, invert=False):