Incorrect result when using input tensor to store result of contraction involving sum

[leburgel]

I encountered what I think is a pretty sneaky bug when assigning the result of a tensor contraction to an existing tensor used in the contraction:

using LinearAlgebra
using TensorOperations

A = randn(ComplexF64, 8, 4, 8)
L = randn(ComplexF64, 8, 8)
R = randn(ComplexF64, 8, 8)
e = randn(ComplexF64)

@tensor B[-1, -2, -3] := L[-1, 1] * A[1, -2, 2] * R[2, -3] + e * A[-1, -2, -3]

@tensor A[-1, -2, -3] = L[-1, 1] * A[1, -2, 2] * R[2, -3] + e * A[-1, -2, -3]

@show norm(B - A) # gives different result

When removing the sum we do get the same result in both cases.

I assume this pattern creates a similar issue as something like

x = randn(ComplexF64, 10)
A = randn(ComplexF64, 10, 10)

@tensor x[-1] = A[-1, 1] * x[1] # throws `ERROR: ArgumentError: output tensor must not be aliased with input tensor`

However, in the first example this is never caught, probably because there is a distinct temporary created in the actual tensor contraction which circumvents the error check.

I don't know if this is a bug that could be fixed or just a pathological pattern that shouldn't be used. But in the latter case this should definitely result in some kind of explicit error being thrown.