rator has a FakeTensor implementation
        (and if it is correct).
    - test_aot_dispatch_static: If the operator works with
        AOTAutograd/AOTDispatch, which is one of the parts in the PT2 stack.
        Checks that the outputs (and gradients, if they are computable)
        of the operator are the same under eager-mode PyTorch and torch.compile.
    - test_aot_dispatch_dynamic: Same as aot_dispatch_static, but
        tests dynamic shapes instead of static shapes.

    For best results, please call ``opcheck`` multiple times with a
    representative set of inputs. For example, if your operator supports
    autograd, please use ``opcheck`` with inputs that require_grad.

    Args:
        op: The operator. Should look like torch.ops.aten.foo
        args: The args to the operator
        kwargs: The kwargs to the operator
        test_utils: Tests that we should run. Default: all of them.
            Example: ["test_schema", "test_faketensor"]
        raise_exception: If we should raise an exception on the first
            error. If False, we will return a dict with information
            on if each test passed or not.

    Nzeopcheck(op, ...): op must be instance of torch._ops.OpOverload, e.g. torch.ops.aten.sin.default, got r