tf.train.shuffle_batch_join(tensors_list, batch_size, capacity, min_after_dequeue, seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False, shared_name=None, name=None) Create batches by randomly shuffling tensors. The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.train.shuffle_batch(). This version enqueues a different list of tensors in different threa

tf.TextLineReader.reset(name=None) Restore a reader to its initial clean state. Args: name: A name for the operation (optional). Returns: The created Operation.

tf.contrib.distributions.TransformedDistribution.mean(name='mean') Mean.

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.dtype

tf.minimum(x, y, name=None) Returns the min of x and y (i.e. x < y ? x : y) element-wise. NOTE: Minimum supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.distributions.Gamma.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.distributions.Distribution.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

void tensorflow::TensorShape::DumpRep() const

tf.contrib.distributions.Chi2.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

Status tensorflow::Env::DeleteRecursively(const string &dirname, int64 *undeleted_files, int64 *undeleted_dirs) Deletes the specified directory and all subdirectories and files underneath it. undeleted_files and undeleted_dirs stores the number of files and directories that weren't deleted (unspecified if the return status is not OK). REQUIRES: undeleted_files, undeleted_dirs to be not null. Typical return codes. OK - dirname exists and we were able to delete everything underneath. NOT_FOU