HeteroskedasticTFPConditional should construct tensors at class-const…

…ruction, not at module-import time (#1598)

gpflow/likelihoods/multilatent.py

@@ -97,21 +97,24 @@ class HeteroskedasticTFPConditional(MultiLatentTFPConditional):
     def __init__(
         self,
         distribution_class: Type[tfp.distributions.Distribution] = tfp.distributions.Normal,
-        scale_transform: tfp.bijectors.Bijector = positive(base="exp"),
+        scale_transform: Optional[tfp.bijectors.Bijector] = None,
         **kwargs,
     ):
         """
         :param distribution_class: distribution class parameterized by `loc` and `scale`
             as first and second argument, respectively.
         :param scale_transform: callable/bijector applied to the latent
             function modelling the scale to ensure its positivity.
-            Typically, `tf.exp` or `tf.softplus`, but can be any function f: R -> R^+.
+            Typically, `tf.exp` or `tf.softplus`, but can be any function f: R -> R^+. Defaults to exp if not explicitly specified. 
         """
+        if scale_transform is None:
+            scale_transform = positive(base="exp")
+        self.scale_transform = scale_transform
 
         def conditional_distribution(Fs) -> tfp.distributions.Distribution:
             tf.debugging.assert_equal(tf.shape(Fs)[-1], 2)
             loc = Fs[..., :1]
-            scale = scale_transform(Fs[..., 1:])
+            scale = self.scale_transform(Fs[..., 1:])
             return distribution_class(loc, scale)
 
         super().__init__(