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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def train(self, data_iterator):
 """Train a keras model on a worker """

        optimizer = get_optimizer(self.master_optimizer)
        self.model = model_from_yaml(self.yaml, self.custom_objects)
        self.model.compile(optimizer=optimizer,
                           loss=self.master_loss, metrics=self.master_metrics)
        self.model.set_weights(self.parameters.value)

        feature_iterator, label_iterator = tee(data_iterator, 2)
        x_train = np.asarray([x for x, y in feature_iterator])
        y_train = np.asarray([y for x, y in label_iterator])

        self.model.compile(optimizer=self.master_optimizer,
                           loss=self.master_loss,
                           metrics=self.master_metrics)

        weights_before_training = self.model.get_weights()
        if x_train.shape[0] > self.train_config.get('batch_size'):
            self.model.fit(x_train, y_train, **self.train_config)
        weights_after_training = self.model.get_weights()
        deltas = subtract_params(
            weights_before_training, weights_after_training)
        yield deltas