Add equal function implementation and corresponding test (#10351)

- Implemented a new function `equal` in the torch backend to compare two tensors for equality.
- Added unit tests for the `equal` function to verify its correctness with different tensor inputs.

extra/torch_backend/backend.py

@@ -583,6 +583,9 @@ def wrap_fxn(k,f):
 
 for k,v in tiny_backend.items(): torch.library.impl(k.replace("aten.", "aten::"), "privateuseone")(wrap_fxn(k,v))
 
+@torch.library.impl("aten::equal", "privateuseone")
+def equal(x: torch.Tensor, y: torch.Tensor): return (x==y).all().item()
+
 if TORCH_DEBUG:
   from torch.utils._python_dispatch import TorchDispatchMode
   class DispatchLog(TorchDispatchMode):

extra/torch_backend/test.py

@@ -163,6 +163,13 @@ class TestTorchBackend(unittest.TestCase):
       a = torch.randn(10, 10, device=device, dtype=torch_dtype)
       self.assertEqual(a.dtype, torch_dtype)
 
+  def test_equal(self):
+    tensor_a = torch.tensor([[1, 2], [3, 4]], device=device)
+    tensor_b = torch.tensor([[1, 2], [3, 4]], device=device)
+    tensor_c = torch.tensor([[1, 2], [1, 2]], device=device)
+    assert torch.equal(tensor_a, tensor_b)
+    assert not torch.equal(tensor_a, tensor_c)
+
   @unittest.skip("meh")
   def test_str(self):
     a = torch.ones(4, device=device)