SSD support on NNVM

I try to deploy an SSD trained network on Android device.
When I try to build the .so file I got this error from nnvm.

[15:43:41] src/nnvm/legacy_json_util.cc:209: Loading symbol saved by previous version v0.9.5. Attempting to upgrade...
[15:43:41] src/nnvm/legacy_json_util.cc:217: Symbol successfully upgraded!
Traceback (most recent call last):
  File "tvm_minimal.py", line 105, in <module>
    Fire(main)
  File "/usr/local/lib/python3.6/dist-packages/fire/core.py", line 127, in Fire
    component_trace = _Fire(component, args, context, name)
  File "/usr/local/lib/python3.6/dist-packages/fire/core.py", line 366, in _Fire
    component, remaining_args)
  File "/usr/local/lib/python3.6/dist-packages/fire/core.py", line 542, in _CallCallable
    result = fn(*varargs, **kwargs)
  File "tvm_minimal.py", line 50, in main
    nnvm_sym, nnvm_params = nnvm.frontend.from_mxnet(mx_sym, args, auxs)
  File "/usr/local/lib/python3.6/dist-packages/nnvm-0.8.0-py3.6.egg/nnvm/frontend/mxnet.py", line 549, in from_mxnet
    sym = _from_mxnet_impl(symbol, {})
  File "/usr/local/lib/python3.6/dist-packages/nnvm-0.8.0-py3.6.egg/nnvm/frontend/mxnet.py", line 506, in _from_mxnet_impl
    node = _convert_symbol(op_name, childs, attr)
  File "/usr/local/lib/python3.6/dist-packages/nnvm-0.8.0-py3.6.egg/nnvm/frontend/mxnet.py", line 414, in _convert_symbol
    _raise_not_supported('Operator: ' + op_name)
  File "/usr/local/lib/python3.6/dist-packages/nnvm-0.8.0-py3.6.egg/nnvm/frontend/mxnet.py", line 24, in _raise_not_supported
    raise NotImplementedError(err)
NotImplementedError: Operator: L2Normalization is not supported in nnvm.

The SSD support on nnvm seem to be done https://github.com/dmlc/tvm/pull/1214 and https://github.com/dmlc/tvm/pull/1157.
Anyone know how to fix this ?

>>> mxnet.__version__
'1.5.0'
>>> tvm.__version__
'0.5.dev'
>>> nnvm.__version__
'0.8.0'

NNVM of TVM-0.5 does not support gluoncv SSD, but supports symbol ssd. In the TVM-0.6, tvm.relay supports gluoncv SSD.

I test symbol ssd(vgg16) in tvm 0.6 . And I meet same error.