code: def to_wider_model(self, pre_layer_id, n_add):
          
          self.operation_history.append(("to_wider_model", pre_layer_id, n_add))
          pre_layer = self.layer_list[pre_layer_id]
          output_id = self.layer_id_to_output_node_ids[pre_layer_id][0]
          dim = layer_width(pre_layer)
          self.vis = {}
          self._search(output_id, dim, dim, n_add)
          # Update the tensor shapes.
          for u in self.topological_order:
              for v, layer_id in self.adj_list[u]:
                  self.node_list[v].shape = self.layer_list[layer_id].output_shape
docs: Widen the last dimension of the output of the pre_layer.
          Args:
              pre_layer_id: The ID of a convolutional layer or dense layer.
              n_add: The number of dimensions to add.