tf.contrib.learn.TensorFlowEstimator.fit(x, y, steps=None, monitors=None, logdir=None) Neural network model from provided model_fn

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample') Generate samples of

tf.contrib.distributions.MultivariateNormalFull.name Name prepended to all ops created by this Distribution.

class tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma StudentT with df = floor(abs(df)) and sigma =

tf.contrib.distributions.BetaWithSoftplusAB.batch_shape(name='batch_shape') Shape of a single sample from a single event index

tf.contrib.distributions.MultivariateNormalCholesky.event_shape(name='event_shape') Shape of a single sample from a single batch

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.__init__(dist_cls, name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

virtual uint64 tensorflow::Env::NowMicros()=0 Returns the number of micro-seconds since some fixed point in time. Only useful

tf.ReaderBase.read_up_to(queue, num_records, name=None) Returns up to num_records (key, value pairs) produced by a reader.