description: Create an op that groups multiple operations.

tf.group

View source on GitHub

Create an op that groups multiple operations.

tf.group(
    *inputs, **kwargs
)

When this op finishes, all ops in inputs have finished. This op has no output.

Note: In TensorFlow 2 with eager and/or Autograph, you should not require this method, as code executes in your expected order. Only use tf.group when working with v1-style code or in a graph context such as inside Dataset.map.

When operating in a v1-style graph context, ops are not executed in the same order as specified in the code; TensorFlow will attempt to execute ops in parallel or in an order convienient to the result it is computing. tf.group allows you to request that one or more results finish before execution continues.

tf.group creates a single op (of type NoOp), and then adds appropriate control dependencies. Thus, c = tf.group(a, b) will compute the same graph as this:

with tf.control_dependencies([a, b]):
    c = tf.no_op()

See also tf.tuple and tf.control_dependencies.