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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 score(self, X, eval_metric='acc', num_batch=None, batch_end_callback=None, reset=True):
 """Run the model given an input and calculate the score as assessed by an evaluation metric. Parameters X : mxnet.DataIter eval_metric : metric.metric The metric for calculating score. num_batch : int or None The number of batches to run. Go though all batches if ``None``. Returns ------- s : float The final score. """

        # setup metric
        if not isinstance(eval_metric, metric.EvalMetric):
            eval_metric = metric.create(eval_metric)

        X = self._init_iter(X, None, is_train=False)
        if reset:
            X.reset()

        data_shapes = X.provide_data
        data_names = [x[0] for x in data_shapes]
        type_dict = dict((key, value.dtype) for (key, value) in self.arg_params.items())
        for x in X.provide_data:
            if isinstance(x, DataDesc):
                type_dict[x.name] = x.dtype
            else:
                type_dict[x[0]] = mx_real_t

        self._init_predictor(data_shapes, type_dict)
        data_arrays = [self._pred_exec.arg_dict[name] for name in data_names]

        for i, batch in enumerate(X):
            if num_batch is not None and i == num_batch:
                break
            _load_data(batch, data_arrays)
            self._pred_exec.forward(is_train=False)
            eval_metric.update(batch.label, self._pred_exec.outputs)

            if batch_end_callback is not None:
                batch_end_params = BatchEndParam(epoch=0,
                                                 nbatch=i,
                                                 eval_metric=eval_metric,
                                                 locals=locals())
                _multiple_callbacks(batch_end_callback, batch_end_params)
        return eval_metric.get()[1]