Give more useful exception when batch is considered during matrix multiplication (#7326)

Fixes #7323

### Description

Give more useful exception when batch is considered during matrix
multiplication.

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: YunLiu <[email protected]>
Co-authored-by: Eric Kerfoot <[email protected]>

Author: KumoLiu

monai/transforms/inverse.py

@@ -185,7 +185,17 @@ def track_transform_meta(
             # not lazy evaluation, directly update the metatensor affine (don't push to the stack)
             orig_affine = data_t.peek_pending_affine()
             orig_affine = convert_to_dst_type(orig_affine, affine, dtype=torch.float64)[0]
-            affine = orig_affine @ to_affine_nd(len(orig_affine) - 1, affine, dtype=torch.float64)
+            try:
+                affine = orig_affine @ to_affine_nd(len(orig_affine) - 1, affine, dtype=torch.float64)
+            except RuntimeError as e:
+                if orig_affine.ndim > 2:
+                    if data_t.is_batch:
+                        msg = "Transform applied to batched tensor, should be applied to instances only"
+                    else:
+                        msg = "Mismatch affine matrix, ensured that the batch dimension is not included in the calculation."
+                    raise RuntimeError(msg) from e
+                else:
+                    raise
             out_obj.meta[MetaKeys.AFFINE] = convert_to_tensor(affine, device=torch.device("cpu"), dtype=torch.float64)
 
         if not (get_track_meta() and transform_info and transform_info.get(TraceKeys.TRACING)):