Sergio Sánchez Ramírez

> Finch can do block matrices if you represent them as a 4-tensor. and how about generalize n-order tensors? > If big kernels are the goal, I would try using...

cool! will revisit this later when we add support for symmetries (we need first to refactor some internal stuff) thanks for the tutorial @willow-ahrens!

A posible solution might be to use `sizehint!` together with `ninds` at the start of the count, and then shrink allocation using `sizehint!` again. Even in the abnormal case where...

Should be solved in the new refactor

ah right 🤦 well, that uncovers another problem ``` julia> Mooncake.value_and_gradient!!(rule, f, A, B) ERROR: MethodError: no method matching (::TenetChainRulesCoreExt.var"#contract_pullback#61"{…})(::ChainRulesCore.Tangent{…}) The function `contract_pullback` exists, but no method is defined for...

@mortenpi this could be what you're looking for (example for DocumenterCitations.jl) https://github.com/LuxDL/DocumenterVitepress.jl/blob/822ed6a94404ed9f9f9c224e9c6fbc3c719056d8/ext/DocumenterVitepressDocumenterCitationsExt.jl#L14-L25

> Sure! The point is to create an identity `MPS` where the tensors will have some user-provided dimensions (`arraysdims`). Thus, what `Base.identity(::Type{MPS}, arraysdims; order)` would do is create zero-initialized tensors...

> @mofeing What happens in the case where χ (`maxdim`) is reached before the middle of the `MPS`? For instance, a 7-site `MPS` with `maxdim=4`: the 1st virtual index would...

The solution for this is to write a case (can be inside the `Tenet.contract` method in TenetReactantExt or several methods for the combinations) when if any of the two tensors...

> ... because the default broadcasting behavior of enzyme batch is enough. Do you mean unrolling or leave the op unchanged?