TL;DR Applying QR factorization to \Sigma matrix returned from an SVD throws an error. Minimal wokring example -

```
using ITensors
i = Index(4, "i")
j = Index(4, "j")
k = Index(4, "k")
A = randomITensor(i, j, k)
U, Σ, V = svd(A, i)
ITensors.qr(Σ, inds(Σ)[1])
```

Error gotten is -

```
MethodError: similar(::NDTensors.DiagTensor{Float64, 2, Tuple{Index{Int64}, Index{Int64}}, NDTensors.Diag{Float64, Vector{Float64}}}, ::Type{Float64}, ::Tuple{Int64, Int64}) is ambiguous. Candidates:
similar(a::AbstractArray, ::Type{T}, dims::Tuple{Vararg{Int64, N}}) where {T, N} in Base at abstractarray.jl:806
similar(a::AbstractArray, ::Type{T}, dims::Tuple{Integer, Vararg{Integer}}) where T in Base at abstractarray.jl:804
similar(a::AbstractArray, ::Type{T}, dims::Tuple{Union{Integer, Base.OneTo}, Vararg{Union{Integer, Base.OneTo}}}) where T in Base at abstractarray.jl:803
similar(A::AbstractArray, ::Type{T}, shape::Tuple{Union{Integer, AbstractUnitRange}, Vararg{Union{Integer, AbstractUnitRange}}}) where T in OffsetArrays at /home/mintycocoa/.julia/packages/OffsetArrays/80Lkc/src/OffsetArrays.jl:320
similar(T::NDTensors.Tensor, args...) in NDTensors at /home/mintycocoa/.julia/packages/NDTensors/c2BpJ/src/tensor.jl:132
Possible fix, define
similar(::NDTensors.Tensor, ::Type{T}, ::Tuple{Int64, Vararg{Int64, N}}) where {T, N}
Stacktrace:
[1] copy_similar(A::NDTensors.DiagTensor{Float64, 2, Tuple{Index{Int64}, Index{Int64}}, NDTensors.Diag{Float64, Vector{Float64}}}, #unused#::Type{Float64})
@ LinearAlgebra ~/.julia/juliaup/julia-1.8.0-rc4+0.x64/share/julia/stdlib/v1.8/LinearAlgebra/src/LinearAlgebra.jl:387
[2] qr(::NDTensors.DiagTensor{Float64, 2, Tuple{Index{Int64}, Index{Int64}}, NDTensors.Diag{Float64, Vector{Float64}}}, ::Tuple{Int64}, ::Vararg{Tuple{Int64}}; kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ LinearAlgebra ~/.julia/juliaup/julia-1.8.0-rc4+0.x64/share/julia/stdlib/v1.8/LinearAlgebra/src/qr.jl:427
[3] qr(::NDTensors.DiagTensor{Float64, 2, Tuple{Index{Int64}, Index{Int64}}, NDTensors.Diag{Float64, Vector{Float64}}}, ::Tuple{Int64}, ::Tuple{Int64})
@ LinearAlgebra ~/.julia/juliaup/julia-1.8.0-rc4+0.x64/share/julia/stdlib/v1.8/LinearAlgebra/src/qr.jl:425
[4] qr(A::ITensor, Linds::Index{Int64}; kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ ITensors ~/.julia/packages/ITensors/OjQuG/src/decomp.jl:355
[5] qr(A::ITensor, Linds::Index{Int64})
@ ITensors ~/.julia/packages/ITensors/OjQuG/src/decomp.jl:334
[6] top-level scope
@ In[56]:9
[7] eval
@ ./boot.jl:368 [inlined]
[8] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)
@ Base ./loading.jl:1428
```

I am an undergraduate student in physics just starting out with tensor networks, and to get my head around matrix product states I was trying to write a function to decompose a tensor into a matrix product state by repeated SVD. So for instance, if we have a tensor T^{s_1s_2s_3s_4}, I can write it as

And then factorize \Sigma and absorb one half into U and the other half into V^\intercal. However when trying to factorize \Sigma as `Q, R = qr(Σ, inds(Σ)[1])`

it throws an error shown above. Any help would be greatly appreciated.