Consider inheritance in type checking for tensors (#31378)

* Consider inheritance in type checking for tensors

Add an additional check to bypass type assertion when both tensors are
torch.Tensor instances.

* Fix the quality issue

src/transformers/trainer_pt_utils.py

@@ -131,9 +131,10 @@ def nested_concat(tensors, new_tensors, padding_index=-100):
     Concat the `new_tensors` to `tensors` on the first dim and pad them on the second if needed. Works for tensors or
     nested list/tuples/dict of tensors.
     """
-    assert type(tensors) == type(
-        new_tensors
-    ), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}."
+    if not (isinstance(tensors, torch.Tensor) and isinstance(new_tensors, torch.Tensor)):
+        assert (
+            type(tensors) == type(new_tensors)
+        ), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}."
     if isinstance(tensors, (list, tuple)):
         return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))
     elif isinstance(tensors, torch.Tensor):