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

* consistent nn. and nn.functional: p3 templates

* restore

tests/test_optimization.py

@@ -24,6 +24,7 @@ from transformers.testing_utils import require_torch
 
 if is_torch_available():
     import torch
+    from torch import nn
 
     from transformers import (
         Adafactor,
@@ -70,7 +71,7 @@ class OptimizationTest(unittest.TestCase):
     def test_adam_w(self):
         w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True)
         target = torch.tensor([0.4, 0.2, -0.5])
-        criterion = torch.nn.MSELoss()
+        criterion = nn.MSELoss()
         # No warmup, constant schedule, no gradient clipping
         optimizer = AdamW(params=[w], lr=2e-1, weight_decay=0.0)
         for _ in range(100):
@@ -84,7 +85,7 @@ class OptimizationTest(unittest.TestCase):
     def test_adafactor(self):
         w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True)
         target = torch.tensor([0.4, 0.2, -0.5])
-        criterion = torch.nn.MSELoss()
+        criterion = nn.MSELoss()
         # No warmup, constant schedule, no gradient clipping
         optimizer = Adafactor(
             params=[w],
@@ -109,7 +110,7 @@ class OptimizationTest(unittest.TestCase):
 
 @require_torch
 class ScheduleInitTest(unittest.TestCase):
-    m = torch.nn.Linear(50, 50) if is_torch_available() else None
+    m = nn.Linear(50, 50) if is_torch_available() else None
     optimizer = AdamW(m.parameters(), lr=10.0) if is_torch_available() else None
     num_steps = 10