Skeleton for span predictor component

This should be moved into its own file, but for now just stubbing out
the methods.

spacy/pipeline/coref.py

@@ -117,7 +117,6 @@ class CoreferenceResolver(TrainablePipe):
         self.span_mentions = span_mentions
         self.span_cluster_prefix = span_cluster_prefix
         self._rehearsal_model = None
-        self.loss = CategoricalCrossentropy()
 
         self.cfg = {}
 
@@ -389,3 +388,91 @@ class CoreferenceResolver(TrainablePipe):
             fname = f"coref_{field}"
             out[fname] = mean([ss[fname] for ss in scores])
         return out
+
+class SpanPredictor(TrainablePipe):
+    """Pipeline component to resolve one-token spans to full spans.
+
+    Used in coreference resolution.
+    """
+
+    def __init__(
+        self,
+        vocab: Vocab,
+        model: Model,
+        name: str = "span_predictor",
+        *,
+        input_prefix: str = "coref_head_clusters",
+        output_prefix: str = "coref_clusters",
+    ) -> None:
+        self.vocab = vocab
+        self.model = model
+        self.name = name
+        self.input_prefix = input_prefix
+        self.output_prefix = output_prefix
+
+        self.cfg = {}
+
+    def predict(self, docs: Iterable[Doc]):
+        ...
+
+    def set_annotations(self, docs: Iterable[Doc], clusters_by_doc) -> None:
+        ...
+
+    def update(
+        self, 
+        examples: Iterable[Example], 
+        *, 
+        drop: float = 0.0, 
+        sgd: Optional[Optimizer] = None, 
+        losses: Optional[Dict[str, float]] = None,
+    ) -> Dict[str, float]:
+        ...
+
+    def rehearse(
+        self, 
+        examples: Iterable[Example], 
+        *, 
+        drop: float = 0.0, 
+        sgd: Optional[Optimizer] = None, 
+        losses: Optional[Dict[str, float]] = None,
+    ) -> Dict[str, float]:
+        ...
+
+    def add_label(self, label: str) -> int:
+        """Technically this method should be implemented from TrainablePipe,
+        but it is not relevant for this component.
+        """
+        raise NotImplementedError(
+            Errors.E931.format(
+                parent="SpanPredictor", method="add_label", name=self.name
+            )
+        )
+
+    def get_loss(
+        self,
+        examples: Iterable[Example],
+        #TODO add necessary args
+    ):
+        ...
+
+    def initialize(
+        self,
+        get_examples: Callable[[], Iterable[Example]],
+        *,
+        nlp: Optional[Language] = None,
+    ) -> None:
+        validate_get_examples(get_examples, "CoreferenceResolver.initialize")
+
+        X = []
+        Y = []
+        for ex in islice(get_examples(), 2):
+            X.append(ex.predicted)
+            Y.append(ex.reference)
+
+        assert len(X) > 0, Errors.E923.format(name=self.name)
+        self.model.initialize(X=X, Y=Y)
+
+    def score(self, examples, **kwargs):
+        # TODO this will overlap significantly with coref, maybe factor into function
+        ...
+