tf.mul(x, y, name=None) Returns x * y element-wise. NOTE: Mul supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128. 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.Chi2.beta Inverse scale parameter.

class tf.contrib.distributions.Binomial Binomial distribution. This distribution is parameterized by a vector p of probabilities and n, the total counts.

tf.ReaderBase.supports_serialize Whether the Reader implementation can serialize its state.

tf.nn.rnn_cell.EmbeddingWrapper.state_size

tf.contrib.graph_editor.reroute_a2b(sgv0, sgv1) Re-route the inputs and outputs of sgv0 to sgv1 (see _reroute).

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.graph

tf.contrib.learn.monitors.ExportMonitor.run_on_all_workers

tf.contrib.distributions.Uniform.b

tf.contrib.distributions.Beta.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.