Flipout Monte Carlo estimator

Hi all,

In the bayesian_neural_network.py example, how many samples are used by default to calculate the Flipout Monte Carlo estimator. If I refer to https://www.tensorflow.org/probability/api_docs/python/tfp/layers/Convolution2DFlipout:

It uses the Flipout gradient estimator to minimize the Kullback-Leibler divergence up to a constant, also known as the negative Evidence Lower Bound. It consists of the sum of two terms: the expected negative log-likelihood, which we approximate via Monte Carlo; and the KL divergence, which is added via regularizer terms which are arguments to the layer.

Monte Carlo approximation is used to approximate the expected negative log-likelihood but I wonder how many samples are used.
Is there a way to increase that number samples to better approximate my ELBO at each iteration?

Thanks