Hi

I am using a sampling strategy during training that I think be implemented by changing a parameter of a HybridBlock during training. To give some context, I am implementing a graph convolutional neural network where I multiply an adjacency matrix A with a node features matrix X. I have the following block:

```
class GraphConv(HybridBlock):
def __init__(self, A, in_units, out_units,
activation=lambda x: x, **kwargs):
super().__init__(**kwargs)
self.activation = activation
self.in_units = in_units
self.out_units = out_units
self.A = self.params.get_constant('A', A)
self.W = self.params.get(
'W', shape=(self.in_units, self.out_units),
)
def hybrid_forward(self, F, X, A, W):
aggregate = F.dot(A, X)
propagate = self.activation(
F.dot(aggregate, W))
return propagate
```

I want to update the Constant A during each epoch during training, where I effectively subsample the full adjacency matrix. Any ideas on how to do this in the Gluon API?