[ONNX] remove outdated ImplicitCastType QA in onnx.rst (pytorch#81268)

Extend work from: pytorch#80596
This PR removes outdated QA of ImplicitCastType , as the coverage is greatly increased with the introduction of onnx shape inference and scalar type analysis.
Pull Request resolved: pytorch#81268
Approved by: https://github.com/justinchuby, https://github.com/BowenBao

docs/source/onnx.rst

@@ -570,27 +570,9 @@ Q: How to export models with primitive type inputs (e.g. int, float)?
 Q: Does ONNX support implicit scalar datatype casting?
 
   No, but the exporter will try to handle that part. Scalars are exported as constant tensors.
-  The exporter will try to figure out the right datatype for scalars. However when it is unable
-  to do so, you will need to manually specify the datatype. This often happens with
-  scripted models, where the datatypes are not recorded. For example::
-
-    class ImplicitCastType(torch.jit.ScriptModule):
-        @torch.jit.script_method
-        def forward(self, x):
-            # Exporter knows x is float32, will export "2" as float32 as well.
-            y = x + 2
-            # Currently the exporter doesn't know the datatype of y, so
-            # "3" is exported as int64, which is wrong!
-            return y + 3
-            # To fix, replace the line above with:
-            # return y + torch.tensor([3], dtype=torch.float32)
-
-    x = torch.tensor([1.0], dtype=torch.float32)
-    torch.onnx.export(ImplicitCastType(), x, "implicit_cast.onnx",
-                      example_outputs=ImplicitCastType()(x))
-
-  We are trying to improve the datatype propagation in the exporter such that implicit casting
-  is supported in more cases.
+  The exporter will figure out the right data type for scalars. In rare cases when it is unable
+  to do so, you will need to manually specify the datatype with e.g. `dtype=torch.float32`.
+  If you see any errors, please [create a GitHub issue](https://github.com/pytorch/pytorch/issues).
 
 Q: Are lists of Tensors exportable to ONNX?