Add `ddm_sdv` onnx model to 🤗

@AlexanderFengler is already working on this.

Since May 7, 2024.

**HSSM version**
0.2.0

**To Reproduce**
```
import numpy
import hssm
hssm.set_floatX("float32")
cav_data = hssm.load_data("cavanagh_theta")
```

**Bug 1:** Bounds by default
```
ddm_model = hssm.HSSM(
data=cav_data,
model='ddm_sdv',
p_outlier=0.05,
)
```
Output:
```
ValueError: Please specify the prior or bounds for sv.
```
I think this should be solved by using the right bounds variable in `defaults.py` - which I see has happened in `main` brain, but please confirm.

**Bug 2:** So I proceeded to explicitly give the bounds, but.
```
ddm_model = hssm.HSSM(
data=cav_data,
model='ddm_sdv',
p_outlier=0.05,
include=[
{
"name": "sv",
"bounds": (0, numpy.inf),
},
],
)
ddm_model.sample()
```
Output:
```
TypeError: Invalid dtype for variable sv_interval__. Can not cast to float32 with casting rule no.
```
Unsure what is causing this.

**Bug 3:** So I moved to `approx_differentiable`, but.
```
ddm_model = hssm.HSSM(
data=cav_data,
model='ddm_sdv',
loglik_kind='approx_differentiable',
p_outlier=0.05,
)
```
Output:
```
Entry Not Found for url: https://huggingface.co/franklab/HSSM/resolve/main/ddm_sdv.onnx.
```
I guess the compiled version from Huggingface was removed?

So I moved to `blackbox` likelihood, but for my actual use case, I have a hierarchical model for `t` parameter and I am facing the same issue as #411, i.e., high r-hats for all `t` parameters :(