tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.value_type

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.name

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.MeanValue.popped_above(unused_value_type)

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.SampleValue.__init__(n=1, stop_gradient=False) Sample n times and concatenate

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.dtype