Fix black and mypy issues

spacy/pipeline/textcat.py

@@ -295,7 +295,7 @@ class TextCategorizer(TrainablePipe):
         """
         if losses is None:
             losses = {}
-        losses.setdefault(self.name+"_rehearse", 0.0)
+        losses.setdefault(self.name + "_rehearse", 0.0)
         if self._rehearsal_model is None:
             return losses
         validate_examples(examples, "TextCategorizer.rehearse")
@@ -311,7 +311,7 @@ class TextCategorizer(TrainablePipe):
         bp_scores(gradient)
         if sgd is not None:
             self.finish_update(sgd)
-        losses[self.name+"_rehearse"] += (gradient**2).sum()
+        losses[self.name + "_rehearse"] += (gradient**2).sum()
         return losses
 
     def _examples_to_truth(

spacy/training/batchers.py

@@ -2,7 +2,7 @@ from typing import Union, Iterable, Sequence, TypeVar, List, Callable, Iterator
 from typing import Optional, Any
 from functools import partial
 import itertools
-from thinc.schedules import Schedule
+from thinc.schedules import Schedule #type:ignore[attr-defined]
 
 from ..util import registry, minibatch
 
@@ -221,7 +221,7 @@ def _batch_by_length(
         if not batch:
             batch.append(i)
         elif length * (len(batch) + 1) <= max_words:
-            batch.append(i)
+            batch.append(i) 
         else:
             batches.append(batch)
             batch = [i]

spacy/training/loop.py

@@ -241,7 +241,7 @@ def train_while_improving(
                     score, other_scores = evaluate()
             else:
                 score, other_scores = evaluate()
-            optimizer.last_score = score
+            optimizer.last_score = score #type:ignore[attr-defined]
             results.append((score, step))
             is_best_checkpoint = score == max(results)[0]
         else: