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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 fastrcnn_predictions(boxes, scores):
 """ Generate final results from predictions of all proposals. Args: boxes: n#classx4 floatbox in float32 scores: nx#class Returns: boxes: Kx4 scores: K labels: K """

    assert boxes.shape[1] == cfg.DATA.NUM_CLASS
    assert scores.shape[1] == cfg.DATA.NUM_CLASS
    boxes = tf.transpose(boxes, [1, 0, 2])[1:, :, :]  # #catxnx4
    scores = tf.transpose(scores[:, 1:], [1, 0])  # #catxn

    def f(X):
        """
        prob: n probabilities
        box: nx4 boxes

        Returns: n boolean, the selection
        """
        prob, box = X
        output_shape = tf.shape(prob, out_type=tf.int64)
        # filter by score threshold
        ids = tf.reshape(tf.where(prob > cfg.TEST.RESULT_SCORE_THRESH), [-1])
        prob = tf.gather(prob, ids)
        box = tf.gather(box, ids)
        # NMS within each class
        selection = tf.image.non_max_suppression(
            box, prob, cfg.TEST.RESULTS_PER_IM, cfg.TEST.FRCNN_NMS_THRESH)
        selection = tf.gather(ids, selection)

        if get_tf_version_tuple() >= (1, 13):
            sorted_selection = tf.sort(selection, direction='ASCENDING')
            mask = tf.sparse.SparseTensor(indices=tf.expand_dims(sorted_selection, 1),
                                          values=tf.ones_like(sorted_selection, dtype=tf.bool),
                                          dense_shape=output_shape)
            mask = tf.sparse.to_dense(mask, default_value=False)
        else:
            # this function is deprecated by TF
            sorted_selection = -tf.nn.top_k(-selection, k=tf.size(selection))[0]
            mask = tf.sparse_to_dense(
                sparse_indices=sorted_selection,
                output_shape=output_shape,
                sparse_values=True,
                default_value=False)
        return mask

    # TF bug in version 1.11, 1.12: https://github.com/tensorflow/tensorflow/issues/22750
    buggy_tf = get_tf_version_tuple() in [(1, 11), (1, 12)]
    masks = tf.map_fn(f, (scores, boxes), dtype=tf.bool,
                      parallel_iterations=1 if buggy_tf else 10)     # #cat x N
    selected_indices = tf.where(masks)  # #selection x 2, each is (cat_id, box_id)
    scores = tf.boolean_mask(scores, masks)

    # filter again by sorting scores
    topk_scores, topk_indices = tf.nn.top_k(
        scores,
        tf.minimum(cfg.TEST.RESULTS_PER_IM, tf.size(scores)),
        sorted=False)
    filtered_selection = tf.gather(selected_indices, topk_indices)
    cat_ids, box_ids = tf.unstack(filtered_selection, axis=1)

    final_scores = tf.identity(topk_scores, name='scores')
    final_labels = tf.add(cat_ids, 1, name='labels')
    final_ids = tf.stack([cat_ids, box_ids], axis=1, name='all_ids')
    final_boxes = tf.gather_nd(boxes, final_ids, name='boxes')
    return final_boxes, final_scores, final_labels