<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def rehearse(self, docs, sgd=None, losses=None, config=None):
 """Make a "rehearsal" update to the models in the pipeline, to prevent forgetting. Rehearsal updates run an initial copy of the model over some data, and update the model so its current predictions are more like the initial ones. This is useful for keeping a pre-trained model on-track, even if you're updating it with a smaller set of examples. docs (iterable):
 A batch of `Doc` objects. drop (float):
 The droput rate. sgd (callable):
 An optimizer. RETURNS (dict):
 Results from the update. EXAMPLE: """

        # TODO: document
        if len(docs) == 0:
            return
        if sgd is None:
            if self._optimizer is None:
                self._optimizer = create_default_optimizer(Model.ops)
            sgd = self._optimizer
        docs = list(docs)
        for i, doc in enumerate(docs):
            if isinstance(doc, basestring_):
                docs[i] = self.make_doc(doc)
        pipes = list(self.pipeline)
        random.shuffle(pipes)
        if config is None:
            config = {}
        grads = {}

        def get_grads(W, dW, key=None):
            grads[key] = (W, dW)

        get_grads.alpha = sgd.alpha
        get_grads.b1 = sgd.b1
        get_grads.b2 = sgd.b2
        for name, proc in pipes:
            if not hasattr(proc, "rehearse"):
                continue
            grads = {}
            proc.rehearse(docs, sgd=get_grads, losses=losses, **config.get(name, {}))
            for key, (W, dW) in grads.items():
                sgd(W, dW, key=key)
        return losses