diff --git a/tinynn/graph/quantization/quantizer.py b/tinynn/graph/quantization/quantizer.py
index 460f9b1a..03b12d32 100644
--- a/tinynn/graph/quantization/quantizer.py
+++ b/tinynn/graph/quantization/quantizer.py
@@ -1788,7 +1788,7 @@ def _is_sub_node(node: TraceNode, custom_data):
 
                 with override_current_trace_graph(graph):
                     trace_func = TraceFunction(new_fullname, True, prefix='fuse_').parse_args(input_tensor, -1)
-                    graph.insert_between(input_node, node, trace_func, [output_tensor])
+                    graph.insert_between(input_node, node, trace_func, [output_tensor], True)
 
                     node.module.func_type = '__add__'
                     node.module.kind = 'add'
@@ -1814,7 +1814,7 @@ def _is_sub_node(node: TraceNode, custom_data):
 
                 with override_current_trace_graph(graph):
                     trace_func = TraceFunction(new_fullname, True, prefix='fuse_').parse_args(input_tensor, -1)
-                    graph.insert_between(input_node, node, trace_func, [output_tensor])
+                    graph.insert_between(input_node, node, trace_func, [output_tensor], True)
 
                     node.module.func_type = '__radd__'
                     node.module.kind = 'add'
@@ -2333,7 +2333,7 @@ def _is_partially_quantizable(node, custom_data):
                             shared_tensors[0]
                         )
                     next_tensors = [x.contiguous() for x in shared_tensors]
-                    graph.insert_between(n, node, trace_func, next_tensors)
+                    graph.insert_between(n, node, trace_func, next_tensors, True)
 
         # Remove non-leaf `.data` nodes
         def _is_non_leaf_data_nodes(node, custom_data):
@@ -2474,7 +2474,7 @@ def _avgpool_kernel_size_and_stride(kernel_size, stride=None, *args, **kwargs):
             node_bn1d.next_tensors[0].unsqueeze_(2).unsqueeze_(2)
 
             prev_out = node_fc.prev_tensors[0][..., None, None]
-            graph.insert_between(node_fc.prev_nodes[0], node_fc, prev_func, [prev_out])
+            graph.insert_between(node_fc.prev_nodes[0], node_fc, prev_func, [prev_out], True)
             next_out = torch.flatten(node_bn1d.next_tensors[0], 1)
             graph.insert_after(node_bn1d, next_func, [next_out])
 
@@ -2524,7 +2524,7 @@ def _is_batch_norm_1d(node, custom_data):
                 node.next_tensors[0].unsqueeze_(2)
 
                 prev_out = torch.unsqueeze(node.prev_tensors[0], 2)
-                graph.insert_between(node.prev_nodes[0], node, prev_func, [prev_out])
+                graph.insert_between(node.prev_nodes[0], node, prev_func, [prev_out], True)
                 next_out = torch.squeeze(node.next_tensors[0], 2)
                 graph.insert_after(node, next_func, [next_out])
 
@@ -2936,7 +2936,7 @@ def _is_broadcastable_binary_quantized_op_node(node: TraceNode, custom_data) ->
                         node.prev_tensors[src_index], node.prev_tensors[ref_index]
                     )
                 next_tensors = [node.prev_tensors[src_index].expand_as(node.prev_tensors[ref_index])]
-                graph.insert_between(node.prev_nodes[src_index], node, trace_func, next_tensors)
+                graph.insert_between(node.prev_nodes[src_index], node, trace_func, next_tensors, True)
 
                 new_node = graph.nodes_map[trace_func.unique_name]
                 new_node.prev_nodes.append(node.prev_nodes[ref_index])