Is reparameterization not available for Mixture MultivariateNormalDiag using MixtureSameFamily?

Trying to sample from a mixture multivariate normal distribution using reparameterization trick, however errors return.

import tensorflow_probability as tfp
tfd = tfp.distributions

gm = tfd.MixtureSameFamily(
      mixture_distribution=tfd.Categorical(
          probs=[0.3, 0.7]),
      components_distribution=tfd.MultivariateNormalDiag(
          loc=[[-1., 1],  # component 1
               [1, -1]],  # component 2
          scale_identity_multiplier=[.3, .6]), reparameterize=True )
sample = gm.sample()

Errors return after executing the code above:

InvalidArgumentError: `univariate_components` must have scalar event
Condition x == y did not hold.
First 1 elements of x:
[False]
First 1 elements of y:
[ True]

Is there any way to work around that?
Or is reparameterization just not available for such distribution? Thank you

Start at the MixtureSameFamily entry point and reproduce the reported Python example with reparameterize=True. Investigate why the MultivariateNormalDiag components trigger the univariate_components error. Done means the behavior is clarified with a supported workaround, or reparameterized sampling works for this distribution.