tf.contrib.distributions.QuantizedDistribution.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

PartialTensorShape tensorflow::PartialTensorShape::Concatenate(int64 size) const Add a dimension to the end ("inner-most"), returns a new PartialTensorShape . REQUIRES: size >= -1, where -1 means unknown

tf.contrib.distributions.MultivariateNormalFull.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.input_dict

tf.contrib.learn.monitors.CheckpointSaver.end(session=None)

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.distribution

tf.contrib.framework.arg_scoped_arguments(func) Returns the list kwargs that arg_scope can set for a func. Args: func: function which has been decorated with @add_arg_scope. Returns: a list of kwargs names.

virtual Status tensorflow::WritableFile::Flush()=0

tf.contrib.distributions.StudentT.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.test.get_temp_dir() Returns a temporary directory for use during tests. There is no need to delete the directory after the test. Returns: The temporary directory.