Fix FakeTensor printing (pytorch#99205)

I got too confused by the FakeTensor printing, so this PR fixes it to
print normally.

Before:
```
with FakeTensorMode():
    x = torch.empty(2, 2, device="cpu")
    print(x)
    # FakeTensor(FakeTensor(..., device='meta', shape=(2, 2)), cpu)
```
After (Tensor printing doesn't print the default device):
```
FakeTensor(..., shape=(2, 2))
```

Test Plan:
- new test
Pull Request resolved: pytorch#99205
Approved by: https://github.com/eellison

test/test_fake_tensor.py

@@ -76,6 +76,13 @@ def test_shape_take_not_device(self):
             self.assertEqual(out.device.type, "cpu")
             self.assertTrue(isinstance(out, FakeTensor))
 
+    def test_repr(self):
+        with FakeTensorMode():
+            x = torch.empty(2, 2, device="cpu")
+            self.assertEqual(repr(x), 'FakeTensor(..., size=(2, 2))')
+            x = torch.empty(2, 2, device="meta")
+            self.assertEqual(repr(x), "FakeTensor(..., device='meta', size=(2, 2))")
+
     @unittest.skipIf(not RUN_CUDA, "requires cuda")
     def test_zero_dim(self):
         with FakeTensorMode() as mode:

torch/_subclasses/fake_tensor.py

@@ -924,12 +924,6 @@ def __init__(self, *args, **kwargs):
     def from_tensor(t, fake_mode):
         return fake_mode.from_tensor(t)
 
-    # TODO: resolve error in default __repr__
-    def __repr__(self):
-        with in_kernel_invocation_manager(self.fake_mode):
-            self_repr = super().__repr__()
-        return f"FakeTensor({self_repr}, {self.fake_device})"
-
     @classmethod
     @count
     def __torch_dispatch__(cls, func, types, args=(), kwargs=None):

torch/_tensor_str.py

@@ -536,12 +536,15 @@ def indented_str(s, indent):
         prefix = "_to_functional_tensor("
         tensor_str = repr(torch._from_functional_tensor(self))
     else:
-        if self.is_meta:
+        # Circular import problem, so we import it here
+        from torch._subclasses.fake_tensor import FakeTensor
+
+        if self.is_meta or isinstance(self, FakeTensor):
             suffixes.append("size=" + str(tuple(self.shape)))
             if self.dtype != torch.get_default_dtype():
                 suffixes.append("dtype=" + str(self.dtype))
             # TODO: This implies that ellipses is valid syntax for allocating
-            # a meta tensor, which it could be, but it isn't right now
+            # a meta tensor or FakeTensor, which it could be, but it isn't right now
             if not custom_contents_provided:
                 tensor_str = "..."
         else: