diff --git a/tests/layoutlmv2/test_tokenization_layoutlmv2.py b/tests/layoutlmv2/test_tokenization_layoutlmv2.py
index 291b8ca0e7e..249660d4a3f 100644
--- a/tests/layoutlmv2/test_tokenization_layoutlmv2.py
+++ b/tests/layoutlmv2/test_tokenization_layoutlmv2.py
@@ -31,14 +31,7 @@ from transformers.models.layoutlmv2.tokenization_layoutlmv2 import (
     _is_punctuation,
     _is_whitespace,
 )
-from transformers.testing_utils import (
-    is_pt_tf_cross_test,
-    require_pandas,
-    require_scatter,
-    require_tokenizers,
-    require_torch,
-    slow,
-)
+from transformers.testing_utils import is_pt_tf_cross_test, require_pandas, require_tokenizers, require_torch, slow
 
 from ..test_tokenization_common import (
     SMALL_TRAINING_CORPUS,
@@ -1219,7 +1212,6 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
 
     @require_torch
     @slow
-    @require_scatter
     def test_torch_encode_plus_sent_to_model(self):
         import torch
 
@@ -1254,10 +1246,15 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
                 words, boxes = self.get_words_and_boxes()
                 encoded_sequence = tokenizer.encode_plus(words, boxes=boxes, return_tensors="pt")
                 batch_encoded_sequence = tokenizer.batch_encode_plus(
-                    [words, words], [boxes, boxes], return_tensors="pt"
+                    [words, words], boxes=[boxes, boxes], return_tensors="pt"
                 )
-                # This should not fail
 
+                # We add dummy image keys (as LayoutLMv2 actually also requires a feature extractor
+                # to prepare the image input)
+                encoded_sequence["image"] = torch.randn(1, 3, 224, 224)
+                batch_encoded_sequence["image"] = torch.randn(2, 3, 224, 224)
+
+                # This should not fail
                 with torch.no_grad():  # saves some time
                     model(**encoded_sequence)
                     model(**batch_encoded_sequence)