tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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.TFRecordReader.restore_state(state, name=None) Restore a reader to a previously saved state. Not all Readers support being restored, so this can produce an Unimplemented error. Args: state: A string Tensor. Result of a SerializeState of a Reader with matching type. name: A name for the operation (optional). Returns: The created Operation.

class tf.errors.UnauthenticatedError The request does not have valid authentication credentials. This exception is not currently used.

tf.TFRecordReader.__init__(name=None, options=None) Create a TFRecordReader. Args: name: A name for the operation (optional). options: A TFRecordOptions object (optional).

tf.FixedLengthRecordReader.read_up_to(queue, num_records, name=None) Returns up to num_records (key, value pairs) produced by a reader. Will dequeue a work unit from queue if necessary (e.g., when the Reader needs to start reading from a new file since it has finished with the previous file). It may return less than num_records even before the last batch. Args: queue: A Queue or a mutable string Tensor representing a handle to a Queue, with string work items. num_records: Number of records t

class tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor LaplaceTensor is a StochasticTensor backed by the distribution Laplace.

tf.OpError.__init__(node_def, op, message, error_code) Creates a new OpError indicating that a particular op failed. Args: node_def: The node_def_pb2.NodeDef proto representing the op that failed, if known; otherwise None. op: The ops.Operation that failed, if known; otherwise None. message: The message string describing the failure. error_code: The error_codes_pb2.Code describing the error.

tf.invert_permutation(x, name=None) Computes the inverse permutation of a tensor. This operation computes the inverse of an index permutation. It takes a 1-D integer tensor x, which represents the indices of a zero-based array, and swaps each value with its index position. In other words, for an output tensor y and an input tensor x, this operation computes the following: y[x[i]] = i for i in [0, 1, ..., len(x) - 1] The values must include 0. There can be no duplicate values or negative values

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.name