[style] consistent nn. and nn.functional: part 3 tests (#12155)

* consistent nn. and nn.functional: p3 templates

* restore

tests/test_modeling_reformer.py

@@ -32,6 +32,7 @@ from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, r
 
 if is_torch_available():
     import torch
+    from torch import nn
 
     from transformers import (
         REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
@@ -241,7 +242,7 @@ class ReformerModelTester:
         # set all position encodings to zero so that postions don't matter
         with torch.no_grad():
             embedding = model.embeddings.position_embeddings.embedding
-            embedding.weight = torch.nn.Parameter(torch.zeros(embedding.weight.shape).to(torch_device))
+            embedding.weight = nn.Parameter(torch.zeros(embedding.weight.shape).to(torch_device))
             embedding.weight.requires_grad = False
 
         half_seq_len = self.seq_length // 2