Hi there,

I noticed that the Bayesian GPLVM class doesn’t allow the user to have a latent dimension (X) of anything apart from 2. Why is that?

```
def __init__(self, base_model, name="GPLVM"):
super(GPLVM, self).__init__(name)
if base_model.X.dim() != 2:
raise ValueError("GPLVM model only works with 2D latent X, but got "
"X.dim() = {}.".format(base_model.X.dim()))
self.base_model = base_model
self.y = self.base_model.y
self.X_loc = Parameter(self.base_model.X)
C = self.X_loc.shape[1]
X_scale_tril_shape = self.X_loc.shape + (C,)
Id = torch.eye(C, out=self.X_loc.new_empty(C, C))
X_scale_tril = Id.expand(X_scale_tril_shape)
self.X_scale_tril = Parameter(X_scale_tril)
self.set_constraint("X_scale_tril", constraints.lower_cholesky)
self._call_base_model_guide = True
```

If I just remove that constraint would it still work ?