[BUG] ChainRule for norm function

Description of bug

Using AD to differentiate the norm gives error: "MethodError: Cannot convert an object of type ChainRulesCore.Tangent{Any, @NamedTuple{data::Vector{Any}, llim::ChainRulesCore.ZeroTangent, rlim::ChainRulesCore.ZeroTangent}} to an object of type MPS"

Minimal code demonstrating the bug or unexpected behavior

using ITensors, ITensorMPS
using LinearAlgebra
using Zygote


function randhaar(s1,s2)
    U = reshape(Matrix(qr(rand(ComplexF64,4,4)).Q),2,2,2,2)
    return ITensor(U,s1,s2,s1',s2')
  end

N = 6
d = 2
sites = siteinds(d,N)
M = randomMPS(sites,2)
function F(us::Vector{ITensor})
  return norm(apply(us,M))
end

us = ITensor[randhaar(s1,s2) for (s1,s2) in zip(sites[1:end-1],sites[2:end])]
#F(us) ≈ 1
gradient(F,us) #fails

Expected output or behavior

Using the function inner instead of norm works, so the problem is probably the rrule for norm

Version information

 ~/.julia/environments/v1.10/Project.toml ⁠
⌃ [082447d4] ChainRules v1.71.0
[d360d2e6] ChainRulesCore v1.25.0
⌃ [0d1a4710] ITensorMPS v0.3.1
⌃ [9136182c] ITensors v0.7.3
⌃ [e88e6eb3] Zygote v0.6.72

Start by running the minimal Julia example with ITensors, ITensorMPS, LinearAlgebra, and Zygote to reproduce the failure at gradient(F, us). Trace the norm call and its ChainRules rrule; done means differentiating F with norm succeeds without the Tangent-to-MPS MethodError, while preserving the working inner-based behavior.