def std_mapper(node, mod):
    """
    Formula of std: sqrt(sum(pow(X-mean(X))))/N)
    This op is mapped to a few existing ops
    """
    input_node = node.kwargs["input"]
    # unbiased = node.kwargs.get("unbiased")
    dim = node.kwargs.get("dim")
    keepdim = node.kwargs.get("keepdim")
    # assert unbiased is False or unbiased is None, "We currently do not support `std` with unbiased=True where n-1 is used"
    assert (
        dim is not None and keepdim is not None
    ), "We currently do not support `std` with dim=None and keepdim=None"

    with node.graph.inserting_before(node):
        # mean(X)
        mean_kwargs = {
            "input": input_node,
            "dim": dim,
            "keepdim": True,
        }
        mean_node = node.graph.call_function(mean, kwargs=mean_kwargs)
        mean_node.meta["type"] = torch.Tensor
        # X-mean(X)
        sub_kwargs = {
            "input": input_node,
            "other": mean_node,
        }
        sub_node = node.graph.call_function(sub, kwargs=sub_kwargs)
        sub_node.meta["type"] = torch.Tensor
        # pow(X-mean(X))
        pow_kwargs = {
            "input": sub_node,
            "exponent": 2.0,
        }
        pow_node = node.graph.call_function(pow, kwargs=pow_kwargs)
        pow_node.meta["type"] = torch.Tensor
        # mean(pow(X-mean(X)))
        post_mean_kwargs = {
            "input": pow_node,
            "dim": dim,
            "keepdim": keepdim,
        }
        post_mean_node = node.graph.call_function(mean, kwargs=post_mean_kwargs)
        post_mean_node.meta["type"] = torch.Tensor
        # sqrt( mean(pow(X-mean(X))) )
        sqrt_kwargs = {
            "input": post_mean_node,
        }
        sqrt_node = node.graph.call_function(sqrt, kwargs=sqrt_kwargs)
        sqrt_node.meta["type"] = torch.Tensor

        output_node = sqrt_node
        output_node.meta = node.meta.copy()
        return output_node
Formula of std: sqrt(sum(pow(X-mean(X))))/N)
This op is mapped to a few existing ops
fx2ait/fx2ait/acc_tracer/acc_ops.py