def _prune_heads(self, heads_to_prune):
        """ Prunes heads of the model.
            heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
            ALBERT has a different architecture in that its layers are shared across groups, which then has inner groups.
            If an ALBERT model has 12 hidden layers and 2 hidden groups, with two inner groups, there
            is a total of 4 different layers.

            These layers are flattened: the indices [0,1] correspond to the two inner groups of the first hidden layer,
            while [2,3] correspond to the two inner groups of the second hidden layer.

            Any layer with in index other than [0,1,2,3] will result in an error.
            See base class PreTrainedModel for more information about head pruning
        """
        for layer, heads in heads_to_prune.items():
            group_idx = int(layer / self.config.inner_group_num)
            inner_group_idx = int(layer - group_idx * self.config.inner_group_num)
            self.encoder.albert_layer_groups[group_idx].albert_layers[inner_group_idx].attention.prune_heads(heads)
 Prunes heads of the model.
            heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
            ALBERT has a different architecture in that its layers are shared across groups, which then has inner groups.
            If an ALBERT model has 12 hidden layers and 2 hidden groups, with two inner groups, there
            is a total of 4 different layers.

            These layers are flattened: the indices [0,1] correspond to the two inner groups of the first hidden layer,
            while [2,3] correspond to the two inner groups of the second hidden layer.

            Any layer with in index other than [0,1,2,3] will result in an error.
            See base class PreTrainedModel for more information about head pruning
        
xtune/src/transformers/modeling_albert.py