Possible Error in DER Loss, Matching Losses from TF Implementation

Dear all,
I came across the ML4PS paper regarding this work recently, and found it super useful to a study I am conducting! Great work.

I was wondering if someone could look into the definition of the DER loss. I am comparing with this TensorFlow implementation from the original paper [here](https://github.com/fbohu/evidential-quantile-regression/blob/177b4a5402a6eb2cc6aec95c940d52910a928311/quantilelosses.py#L49) . Firstly, in line 453 in your models.py, I think there should be a 1/beta term. You have
```python
+ (coeff * torch.abs(error / w_st) * (2.0 * nu + alpha))
```
but the [original paper](https://arxiv.org/pdf/2308.10650) defines in equation 12 this part to be $\rho_q (y_i − \gamma_i) · \phi$ where $\phi_i = 2 \nu_i + \alpha_i + 1/\beta_i$

Additionally, they define the width of the distribution through sampling an exponential as such

```python
tfd = tfp.distributions
theta = (1.0-2.0*quantile)/(quantile*(1.0-quantile))
mean_ = beta/(alpha-1)

w_i_dis = tfd.Exponential(rate=1/mean_)
```
However, it appears you guys define it via

```python
w_st = torch.sqrt(beta * (1 + nu) / (alpha * nu))
```

I'm a little new to this area, so it's very possible I'm missing something. I'm also happy to submit a PR and contribute to the development of this package.

All the best,
Eddie