Bug: float division instead of integer division in GQA reshape causes silent shape truncation

## Summary

In the GQA (Grouped Query Attention) reshape operations across multiple modules, `int(kg / self.num_kv_heads)` uses float division (`/`) instead of integer division (`//`). When `kg` is not exactly divisible by `num_kv_heads`, Python's `int()` silently truncates the result, producing an incorrect reshape dimension with no error.

## Affected files

| File | Lines |
|------|-------|
| `gemma/gm/nn/_modules.py` | 244, 288 |
| `gemma/gm/nn/gemma3n/_modules.py` | 342, 387 |
| `gemma/gm/nn/gemma4/_modules.py` | 324, 363 |
| `gemma/research/t5gemma/modules.py` | 240, 272 |

## Example (from `gm/nn/_modules.py`)

```python
# Current (buggy)
query_scaled = query_scaled.reshape(
(b, t, self.num_kv_heads, int(kg / self.num_kv_heads), h)
)

# Fixed
query_scaled = query_scaled.reshape(
(b, t, self.num_kv_heads, kg // self.num_kv_heads, h)
)
```

## Impact

If `kg % num_kv_heads != 0`, the truncated dimension causes the reshape to produce a tensor whose total element count does not match the original. This leads to:

- A `ValueError` at runtime from JAX/NumPy (reshape size mismatch), **or**
- Silent incorrect computation if the truncation happens to produce a valid (but wrong) shape by coincidence

In practice, standard model configurations keep `num_query_heads` a multiple of `num_kv_heads`, so this bug is latent. However, custom or experimental configurations will hit it unexpectedly.

## Fix

Replace all 8 occurrences of `int(kg / self.num_kv_heads)` with `kg // self.num_kv_heads`.