tf.contrib.distributions.Binomial.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes.

tf.contrib.learn.monitors.CaptureVariable.run_on_all_workers

tf.nn.rnn_cell.DropoutWrapper.state_size

tf.contrib.distributions.MultivariateNormalFull.std(name='std') Standard deviation.

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

tf.contrib.distributions.Gamma.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='Gamma') Construct Gamma

tf.contrib.learn.LinearRegressor.set_params(**params) Set the parameters of this estimator. The

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.dtype

tf.contrib.distributions.ExponentialWithSoftplusLam.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape)

tf.contrib.distributions.Binomial.is_reparameterized