Update distill signature to accept Examples instead of separate teacher and student docs

spacy/pipeline/tok2vec.py

@@ -192,14 +192,37 @@ class Tok2Vec(TrainablePipe):
 
     def distill(
         self,
-        teacher_pipe: "TrainablePipe",
-        teacher_docs: Iterable["Doc"],
-        student_docs: Iterable["Doc"],
+        teacher_pipe: Optional["TrainablePipe"],
+        examples: Iterable["Example"],
         *,
         drop: float = 0.0,
         sgd: Optional[Optimizer] = None,
         losses: Optional[Dict[str, float]] = None,
     ) -> Dict[str, float]:
+        """Train a pipe (the student) on the predictions of another pipe
+        (the teacher). The student is typically trained on the probability
+        distribution of the teacher, but details may differ per pipe.
+
+        teacher_pipe (Optional[TrainablePipe]): The teacher pipe to learn
+            from.
+        examples (Iterable[Example]): Distillation examples. The reference
+            and predicted docs must have the same number of tokens and the
+            same orthography.
+        drop (float): dropout rate.
+        sgd (Optional[Optimizer]): An optimizer. Will be created via
+            create_optimizer if not set.
+        losses (Optional[Dict[str, float]]): Optional record of loss during
+            distillation.
+        RETURNS: The updated losses dictionary.
+
+        DOCS: https://spacy.io/api/tok2vec#distill
+        """
+        # By default we require a teacher pipe, but there are downstream
+        # implementations that don't require a pipe.
+        if teacher_pipe is None:
+            raise ValueError(Errors.E4002.format(name=self.name))
+        teacher_docs = [eg.reference for eg in examples]
+        student_docs = [eg.predicted for eg in examples]
         teacher_preds = teacher_pipe.predict(teacher_docs)
         teacher_pipe.set_annotations(teacher_docs, teacher_preds)
         return self._update_with_docs(student_docs, drop=drop, sgd=sgd, losses=losses)