How to use the nni.networkmorphism_tuner.layers.is_layer function in nni

continue
                layer = self.layer_list[layer_id]
                copied_layer = copy(layer)
                copied_layer.weights = None
                ret.add_layer(deepcopy(copied_layer))

        for u in index_in_main_chain:
            for v, layer_id in self.adj_list[u]:
                if v not in index_in_main_chain:
                    temp_u = u
                    temp_v = v
                    temp_layer_id = layer_id
                    skip_type = None
                    while not (
                            temp_v in index_in_main_chain and temp_u in index_in_main_chain):
                        if is_layer(
                                self.layer_list[temp_layer_id], "Concatenate"):
                            skip_type = NetworkDescriptor.CONCAT_CONNECT
                        if is_layer(self.layer_list[temp_layer_id], "Add"):
                            skip_type = NetworkDescriptor.ADD_CONNECT
                        temp_u = temp_v
                        temp_v, temp_layer_id = self.adj_list[temp_v][0]
                    ret.add_skip_connection(
                        index_in_main_chain[u], index_in_main_chain[temp_u], skip_type
                    )

                elif index_in_main_chain[v] - index_in_main_chain[u] != 1:
                    skip_type = None
                    if is_layer(self.layer_list[layer_id], "Concatenate"):
                        skip_type = NetworkDescriptor.CONCAT_CONNECT
                    if is_layer(self.layer_list[layer_id], "Add"):
                        skip_type = NetworkDescriptor.ADD_CONNECT

else:
                    next_start_dim = start_dim
                    next_total_dim = total_dim + self._upper_layer_width(
                        self.layer_id_to_input_node_ids[layer_id][1]
                    )
                self._search(v, next_start_dim, next_total_dim, n_add)

            else:
                self._search(v, start_dim, total_dim, n_add)

        for v, layer_id in self.reverse_adj_list[u]:
            layer = self.layer_list[layer_id]
            if is_layer(layer, "Conv"):
                new_layer = wider_pre_conv(layer, n_add, self.weighted)
                self._replace_layer(layer_id, new_layer)
            elif is_layer(layer, "Dense"):
                new_layer = wider_pre_dense(layer, n_add, self.weighted)
                self._replace_layer(layer_id, new_layer)
            elif is_layer(layer, "Concatenate"):
                continue
            else:
                self._search(v, start_dim, total_dim, n_add)

def layer_distance(a, b):
    """The distance between two layers."""
    # pylint: disable=unidiomatic-typecheck
    if not isinstance(a, type(b)):
        return 1.0
    if is_layer(a, "Conv"):
        att_diff = [
            (a.filters, b.filters),
            (a.kernel_size, b.kernel_size),
            (a.stride, b.stride),
        ]
        return attribute_difference(att_diff)
    if is_layer(a, "Pooling"):
        att_diff = [
            (a.padding, b.padding),
            (a.kernel_size, b.kernel_size),
            (a.stride, b.stride),
        ]
        return attribute_difference(att_diff)
    return 0.0

def _upper_layer_width(self, u):
        for v, layer_id in self.reverse_adj_list[u]:
            layer = self.layer_list[layer_id]
            if is_layer(layer, "Conv") or is_layer(layer, "Dense"):
                return layer_width(layer)
            elif is_layer(layer, "Concatenate"):
                a = self.layer_id_to_input_node_ids[layer_id][0]
                b = self.layer_id_to_input_node_ids[layer_id][1]
                return self._upper_layer_width(a) + self._upper_layer_width(b)
            else:
                return self._upper_layer_width(v)
        return self.node_list[0].shape[-1]

)
                self._replace_layer(layer_id, new_layer)

            elif is_layer(layer, "Dense"):
                new_layer = wider_next_dense(
                    layer, start_dim, total_dim, n_add, self.weighted
                )
                self._replace_layer(layer_id, new_layer)

            elif is_layer(layer, "BatchNormalization"):
                new_layer = wider_bn(
                    layer, start_dim, total_dim, n_add, self.weighted)
                self._replace_layer(layer_id, new_layer)
                self._search(v, start_dim, total_dim, n_add)

            elif is_layer(layer, "Concatenate"):
                if self.layer_id_to_input_node_ids[layer_id][1] == u:
                    # u is on the right of the concat
                    # next_start_dim += next_total_dim - total_dim
                    left_dim = self._upper_layer_width(
                        self.layer_id_to_input_node_ids[layer_id][0]
                    )
                    next_start_dim = start_dim + left_dim
                    next_total_dim = total_dim + left_dim
                else:
                    next_start_dim = start_dim
                    next_total_dim = total_dim + self._upper_layer_width(
                        self.layer_id_to_input_node_ids[layer_id][1]
                    )
                self._search(v, next_start_dim, next_total_dim, n_add)

            else:

def layer_distance(a, b):
    """The distance between two layers."""
    # pylint: disable=unidiomatic-typecheck
    if not isinstance(a, type(b)):
        return 1.0
    if is_layer(a, "Conv"):
        att_diff = [
            (a.filters, b.filters),
            (a.kernel_size, b.kernel_size),
            (a.stride, b.stride),
        ]
        return attribute_difference(att_diff)
    if is_layer(a, "Pooling"):
        att_diff = [
            (a.padding, b.padding),
            (a.kernel_size, b.kernel_size),
            (a.stride, b.stride),
        ]
        return attribute_difference(att_diff)
    return 0.0

"""Search the graph for all the layers to be widened caused by an operation.
        It is an recursive function with duplication check to avoid deadlock.
        It searches from a starting node u until the corresponding layers has been widened.
        Args:
            u: The starting node ID.
            start_dim: The position to insert the additional dimensions.
            total_dim: The total number of dimensions the layer has before widening.
            n_add: The number of dimensions to add.
        """
        if (u, start_dim, total_dim, n_add) in self.vis:
            return
        self.vis[(u, start_dim, total_dim, n_add)] = True
        for v, layer_id in self.adj_list[u]:
            layer = self.layer_list[layer_id]

            if is_layer(layer, "Conv"):
                new_layer = wider_next_conv(
                    layer, start_dim, total_dim, n_add, self.weighted
                )
                self._replace_layer(layer_id, new_layer)

            elif is_layer(layer, "Dense"):
                new_layer = wider_next_dense(
                    layer, start_dim, total_dim, n_add, self.weighted
                )
                self._replace_layer(layer_id, new_layer)

            elif is_layer(layer, "BatchNormalization"):
                new_layer = wider_bn(
                    layer, start_dim, total_dim, n_add, self.weighted)
                self._replace_layer(layer_id, new_layer)
                self._search(v, start_dim, total_dim, n_add)

)
                    next_start_dim = start_dim + left_dim
                    next_total_dim = total_dim + left_dim
                else:
                    next_start_dim = start_dim
                    next_total_dim = total_dim + self._upper_layer_width(
                        self.layer_id_to_input_node_ids[layer_id][1]
                    )
                self._search(v, next_start_dim, next_total_dim, n_add)

            else:
                self._search(v, start_dim, total_dim, n_add)

        for v, layer_id in self.reverse_adj_list[u]:
            layer = self.layer_list[layer_id]
            if is_layer(layer, "Conv"):
                new_layer = wider_pre_conv(layer, n_add, self.weighted)
                self._replace_layer(layer_id, new_layer)
            elif is_layer(layer, "Dense"):
                new_layer = wider_pre_dense(layer, n_add, self.weighted)
                self._replace_layer(layer_id, new_layer)
            elif is_layer(layer, "Concatenate"):
                continue
            else:
                self._search(v, start_dim, total_dim, n_add)

def _insert_pooling_layer_chain(self, start_node_id, end_node_id):
        skip_output_id = start_node_id
        for layer in self._get_pooling_layers(start_node_id, end_node_id):
            new_layer = deepcopy(layer)
            if is_layer(new_layer, "Conv"):
                filters = self.node_list[start_node_id].shape[-1]
                new_layer = get_conv_class(self.n_dim)(
                    filters, filters, 1, layer.stride)
                if self.weighted:
                    init_conv_weight(new_layer)
            else:
                new_layer = deepcopy(layer)
            skip_output_id = self.add_layer(new_layer, skip_output_id)
        skip_output_id = self.add_layer(StubReLU(), skip_output_id)
        return skip_output_id