Handle truncated image boundaries in `_convert` to avoid tensor size mismatch

## Summary

This PR proposes a change in `_convert` to handle cases where truncation (`max_inp_length`)
could leave an unmatched `<im_start>` (or `<slice_start>`) token without its closing `<im_end>` / `<slice_end>`.

When this happens, `image_start_idx` and `image_end_idx` have different lengths,
causing a runtime error in line 274:

```
RuntimeError: Sizes of tensors must match except in dimension 1. Expected size x but got size x-1 for tensor number 1 in the list.
```

## Changes

- Changed `valid_image_nums` from `max(len(start), len(end))` to `min(len(start), len(end))`
→ only keep valid start–end pairs

processing_minicpmo.py

@@ -269,7 +269,7 @@ class MiniCPMOProcessor(ProcessorMixin):
         image_start_idx += 1
         image_end_idx = torch.where(end_cond)[0]
 
-        valid_image_nums = max(len(image_start_idx), len(image_end_idx))
+        valid_image_nums = min(len(image_start_idx), len(image_end_idx))
 
         image_bounds = torch.hstack(
             [