I am trying to implement task-specific weighting of multiple embeddings as in Elmo.

Currently, I initialized weights for multiple embeddings using `self.param.get`

. However, it throws me the error.

`AssertionError: Argument data must be Symbol instances, but got Parameter elmoembedding0_weights (shape=(3,), dtype=<class 'numpy.float32'>)`

.

I can call `x.data()`

for non hybridized or `x.var()`

for hybridized version for the parameters. Is there a way to simply apply Softmax to parameters and work with both versions? Thanks!

My code looks something like this

```
import mxnet as mx
import mxnet.gluon as gluon
class ElmoEmbedding(gluon.HybridBlock):
def __init__(self):
super(ElmoEmbedding, self).__init__()
with self.name_scope():
self.weights = self.params.get('weights',
shape=(3,),
init=mx.init.Constant(1.0))
self.scales = self.params.get('scales',
shape=(1,0),
init=mx.init.Constant(1.0))
def hybrid_forward(self, F, x, *args, **kwargs):
normalized_weights = F.softmax(self.weights)
weighted_x = F.dot(normalized_weights, x)
output = F.broadcast_mul(self.scales, weighted_x)
return output
net = ElmoEmbedding()
net.hybridize()
# create input
x = mx.ndarray.random.randn(3,100)
output = net(x)
print("output", output.shape)
```