I use relay.frontend.from_tensorflow,the following error occurred:
```
File "tvm/python/tvm/relay/frontend/tensorflow.py", in from_tensorflow
         mod, params = g.from_tensorflow(graph, layout, shape, outputs)
File "tvm/python/tvm/relay/frontend/tensorflow.py", in from_tensorflow
         for node_item in self._nodes[node.name]]
File "tvm/python/tvm/relay/frontend/common.py", in infer_shape
         return get_const_tuple(out_type.checked_type.shape)
File "tvm/topi/python/topi/util.py", in infer_shape,in get_const_tuple
        elem = tvm.tir.ir_pass.Simplify(elem)
xxxxx(etc.)
TVMError: Check failed:error:can_dispatch(n):NodeFunctor calls un-registered 
function on type Any
```
```
TVM: latest version
Tensorflow:1.14
Model: bert
```
I print the other information:
OP : Where ( len(inputs) == 1, so it's argwhere )
```
[
    CallNode(
        Op(argwhere),
        [CallNode(
                 Op(not_equal),
                  [CallNode(
                         Op(full),
                         [Constant(1),],
                         relay.attrs.InitOpAttrs(0x98720b8),
                         []),
                    CallNode(
                         Op(zeros_like),
                         
[CallNode(Op(full),[Constant(1),],relay.attrs.InitOpAttrs(0x98720b8),[])]
                         relay.attrs.InitOpAttrs(0x98720b8),
                         []),
                  ],
                  (nullptr),[]
        )],
        (nullptr),
        []
    )
]
```
input mod info:
```
%0 = full(1,shape=[128],dtype="int64");
free_var % zeros_like: Tensor[(128),int 64]
not_equal(%0, % zeros_like)
```
input shape:
```
[128]
```
I add print in infer_shape function from 
"tvm/python/tvm/relay/frontend/common.py":
```
error out_type.checked_type = Tensor[(?, 1), int32]
out_type.checked_type.shape: [?, 1]
```
The output shape of argwhere op  should be dynamic,it depends on the actual 
calculation.

So,Is there a problem with the  'where'  implementation of relay ?





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