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Opening to keep track of the problem in #150
@benjaminfaber can you see what the new bug is for your MWE?
```julia
using ImplicitDifferentiation
using Enzyme
using ComponentArrays
functio…
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In the definition of type `Real`:
~~~c++
template
class Real
{
...
};
~~~
Ensure `T` is a numeric type (not necessarily standard number types such as float, double, but also permit other c…
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I tried running the last example in the README with an HMC sampler substituted for the MALA one:
```
using Klara
plogtarget(z) = -dot(z, z)
p = BasicContMuvParameter(:p, logtarget=plogtarget…
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I'm running into some trouble applying `optimistix.least_squares(fn, LevenbergMarquardt(...), x0)` to certain problems. From the error message below, my understanding of the root cause is that forward…
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I'm a newbie using the autodiff library and more general using automatic differentiation in general. I was trying to implement gradient computation for a simple Gauss-Seidel type solver. It runs fine …
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Dear devs,
recently I have been trying to differentiate complicated expressions that eventually result in nested calls to `pow`. I find that the forward mode derivative for these cases is not taken…
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Is it possible to use std::complex as a custom scalar type in reserve mode? Can you add an example?
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**Describe the bug**
Reductions with GPU broadcasting error with Enzyme. @wsmoses suggested I open an issue here.
**To reproduce**
The Minimal Working Example (MWE) for this bug:
```julia
…
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Hello
I am currently using autodiff for a planning problem where gradient calculation is very time critical and it appears that my application spends most of the time during the gradient calculatio…
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## Feature request
It would be great if Numba supported automatic differentiation. Maybe using [Enzyme](https://github.com/EnzymeAD/Enzyme) would be the easiest way as it operates directly on th…