import torch

with torch.library._scoped_library("mylib", "DEF") as lib:
    lib.define(
        "copy_(Tensor(a!) dst, Tensor src) -> ()",
        # tags=torch.Tag.needs_fixed_stride_order,
    )

    @torch.library.impl(lib, "copy_", "Meta")
    def _(dst, src):
        return None

    @torch.library.impl(lib, "copy_", "CompositeExplicitAutograd")
    def _(dst, src):
        if src.is_contiguous():
            dst.copy_(src + 1)
        else:
            dst.copy_(src)

    def f(x):
        full_default_3 = torch.full([3, 3], 7.0, device="cpu")
        chunk_cat_default_1 = torch.ops.mylib.copy_.default(full_default_3, x)
        mul_out = torch.mul(full_default_3, full_default_3)
        return mul_out

    x = torch.arange(9, dtype=torch.float, device="cpu").view(3, 3).t().contiguous().t()
    eager_out = f(x)

    compiled_inductor_f = torch.compile(f, backend="inductor", fullgraph=True)
    compiled_inductor_out = compiled_inductor_f(x)

assert torch.allclose(compiled_inductor_out, eager_out)