`metrax.nnx` metrics incompatible with `nnx.MultiMetric` due to strict `kwargs` in `from_model_output`

## Description
`metrax.nnx.nnx_wrapper.NnxWrapper` metrics are currently incompatible with `flax.nnx.MultiMetric` when used alongside other metrics that require different keyword arguments.

When `nnx.MultiMetric.update(**kwargs)` is called, it passes all `kwargs` to every registered metric. However, the underlying `metrax` metrics (wrapped by `NnxWrapper`) typically implement `from_model_output` with specific arguments (e.g., `predictions`, `labels`, `values`) and do not accept arbitrary `**kwargs`. This results in a `TypeError` when extraneous arguments (intended for other metrics) are passed.

## Minimal Reproduction

Here is an example where two `metrax` metrics (`Average` and `Accuracy`) are used together. They fail because they expect different arguments and do not ignore the arguments intended for the other.

```python
import metrax.nnx
from flax import nnx
import jax.numpy as jnp

# Define metrics
metrics = nnx.MultiMetric(
loss=metrax.nnx.Average(), # Expects 'values'
accuracy=metrax.nnx.Accuracy() # Expects 'predictions', 'labels'
)

# Update with arguments for both.
metrics.update(
values=jnp.array([0.5, 0.2]),
predictions=jnp.array([0, 1]),
labels=jnp.array([0, 1])
)
```

## Traceback
```text
TypeError: Average.from_model_output() got an unexpected keyword argument 'predictions'
```

## Suggested Fix
Modify the base `Metric` class or the individual metric implementations in `metrax` to accept and ignore arbitrary `**kwargs` in `from_model_output`.

For example, in `metrax/base.py`:

```python
@classmethod
def from_model_output(
cls,
values: jax.Array,
sample_weights: jax.Array | None = None,
**kwargs, # Add this to absorb extra arguments
) -> 'Average':
# ... implementation ...
```