sum_gradient and sum_hessians computation in find_node_split_subtraction about pygbm HOT 4 OPEN

            if node.hist_subtraction:
                if node is node.parent.right_child:
                    sum_gradients = node.parent.split_info.gradient_right
                    sum_hessians = node.parent.split_info.hessian_right
                else:
                    sum_gradients = node.parent.split_info.gradient_left
                    sum_hessians = node.parent.split_info.hessian_left
                split_info = self.splitter.find_node_split_subtraction(
                    node.sample_indices,
                    sum_gradients, sum_hessians, node.parent.histograms,
                    node.sibling.histograms, histograms)
            else:
                split_info = self.splitter.find_node_split(
                    node.sample_indices, histograms)

    # We can pick any feature (here the first) in the histograms to
    # compute the gradients: they must be the same across all features
    # anyway, we have tests ensuring this. Maybe a more robust way would
    # be to compute an average but it's probably not worth it.
    context.sum_gradients = (parent_histograms[0]['sum_gradients'].sum() -
                             sibling_histograms[0]['sum_gradients'].sum())

    n_samples = sample_indices.shape[0]
    if context.constant_hessian:
        context.sum_hessians = \
            context.constant_hessian_value * float32(n_samples)
    else:
        context.sum_hessians = (parent_histograms[0]['sum_hessians'].sum() -
sibling_histograms[0]['sum_hessians'].sum())