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The following snippet prints null gradients while if using `backward(c, true)` we get the right value (5.0, 2.0):
```C++
using FloatD = DiffArray;
FloatD a = 2.0f;
FloatD b = 5.0f;
set_requires…
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## Description
Using known identities is a powerful way to test precision of our functions. Currently using identities for derivatives is a bit tedious - you need to explicitly compute derivative aga…
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Hi,
this is a question and / or a feature request.
How can I use jax to calculate the gradient of functions R -> C?
Example below:
```python
import jax
import jax.numpy as jnp
def f(x):
…
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`test_get_fitted_player_model_numpyro` marked as an xfail.
Error is:
```
./airsenal/tests/test_score_predictions.py::test_get_fitted_player_model_numpyro Failed: [undefined]RuntimeError: Cannot…
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Hi,
I implemented a Jacobian computation using functorch, but encoutnered a memory overflow issue.
The function that I want to differentiate is `ResidualFunctional.residual`. I'd like to compute…
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| | |
|------------------|-----------------|
|Previous ID | SR-13096 |
|Radar | rdar://problem/72819053 |
|Original R…
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Could you please add an example with a simple CUDA kernel in the Julia [introduction notebook](https://github.com/EnzymeAD/Enzyme-Tutorial/blob/main/julia/introduction.ipynb) ?
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**Description**
The following code fails to compile in Debug mode.
Note that this project requires one public import: `Numerics`.
`https://github.com/apple/swift-numerics.git`
```swift
imp…
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I don't know whether it's premature to do so, but since we're thinking about how Flux interacts with AD, I tried out a few very simple cases. Exactly the same cases as in https://github.com/EnzymeAD/E…