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**Description**
The following code fails to build in Debug mode. The error message is:
```
Assertion failed: (!confRef.isInvalid() && "Missing conformance to `AdditiveArithmetic`"), function emitZe…
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I'm new to using Enzyme. I'm trying to differentiate the following piece of code:
```julia
using Redbird
using SparseArrays
using MATLAB
using Enzyme
##########################################…
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This works on Julia 1.9 and Enzyme main (6692fad483dbe4002be9d254dae959c634d841c9):
```julia
using Enzyme
autodiff(Reverse, x -> sum(skipmissing([x, missing, 3.0])), Active, Active(1.0))
```
```
…
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We should make a new crate that can take in a Rose function and transform it (recursively, i.e. including its dependencies) to produce a new function that computes its Jacobian-vector product (JVP) or…
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The following Julia code attempts to differentiate a convolution from NNlib but fails:
```julia
using Enzyme
Enzyme.API.runtimeActivity!(true)
using NNlib
w = randn(Float32, 3, 3, 5, 7);
dw …
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I'm playing with `Enzyme.jl` on an [IPU](https://www.graphcore.ai/products/ipu). Autodiff of a few functions work out-of-the-box, which is awesome, but differentiation of `log` chokes:
```julia
f(x…
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I have a case where I need to autodiff through scalar functions and matrix operations. I understand forward mode is better for the former and reverse mode more efficient for the latter.
what might …
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Please consider adding the JAX equivalent of `scipy.integrate` to the library. This would be very useful!
### Use case
As an example use case, I provide a simple implementation of a failed attem…
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Differentiation of call expressions that takes array arguments give incorrect derivatives if the said array is modified between the
primal call expression and the derived call expression.
A repro…
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```jl
using Lux, ComponentArrays, OrdinaryDiffEq, Optimization, OptimizationNLopt,
OptimizationOptimisers, SciMLSensitivity, Zygote, Plots, Statistics, Random
using Enzyme; Enzyme.Compiler.bitc…