Auto-format code with black (#11687)

Co-authored-by: explosion-bot <explosion-bot@users.noreply.github.com>

spacy/tests/pipeline/test_tok2vec.py

@@ -231,7 +231,7 @@ def test_tok2vec_listener_callback():
 
 
 def test_tok2vec_listener_overfitting():
-    """ Test that a pipeline with a listener properly overfits, even if 'tok2vec' is in the annotating components """
+    """Test that a pipeline with a listener properly overfits, even if 'tok2vec' is in the annotating components"""
     orig_config = Config().from_str(cfg_string)
     nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
     train_examples = []
@@ -264,7 +264,7 @@ def test_tok2vec_listener_overfitting():
 
 
 def test_tok2vec_frozen_not_annotating():
-    """ Test that a pipeline with a frozen tok2vec raises an error when the tok2vec is not annotating """
+    """Test that a pipeline with a frozen tok2vec raises an error when the tok2vec is not annotating"""
     orig_config = Config().from_str(cfg_string)
     nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
     train_examples = []
@@ -274,12 +274,16 @@ def test_tok2vec_frozen_not_annotating():
 
     for i in range(2):
         losses = {}
-        with pytest.raises(ValueError, match=r"the tok2vec embedding layer is not updated"):
-            nlp.update(train_examples, sgd=optimizer, losses=losses, exclude=["tok2vec"])
+        with pytest.raises(
+            ValueError, match=r"the tok2vec embedding layer is not updated"
+        ):
+            nlp.update(
+                train_examples, sgd=optimizer, losses=losses, exclude=["tok2vec"]
+            )
 
 
 def test_tok2vec_frozen_overfitting():
-    """ Test that a pipeline with a frozen & annotating tok2vec can still overfit """
+    """Test that a pipeline with a frozen & annotating tok2vec can still overfit"""
     orig_config = Config().from_str(cfg_string)
     nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
     train_examples = []
@@ -289,7 +293,13 @@ def test_tok2vec_frozen_overfitting():
 
     for i in range(100):
         losses = {}
-        nlp.update(train_examples, sgd=optimizer, losses=losses, exclude=["tok2vec"], annotates=["tok2vec"])
+        nlp.update(
+            train_examples,
+            sgd=optimizer,
+            losses=losses,
+            exclude=["tok2vec"],
+            annotates=["tok2vec"],
+        )
     assert losses["tagger"] < 0.0001
 
     # test the trained model