Method control_dependencies

tensorflow/python/framework/ops.py:4639–4750

Returns a context manager that specifies control dependencies. Use with the `with` keyword to specify that all operations constructed within the context should have control dependencies on `control_inputs`. For example: ```python with g.control_dependencies([a, b, c]): #

(self, control_inputs)

      controller.add_op(op)

  def control_dependencies(self, control_inputs):
    """Returns a context manager that specifies control dependencies.

    Use with the `with` keyword to specify that all operations constructed
    within the context should have control dependencies on
    `control_inputs`. For example:

    ```python
    with g.control_dependencies([a, b, c]):
      # `d` and `e` will only run after `a`, `b`, and `c` have executed.
      d = ...
      e = ...
    ```

    Multiple calls to `control_dependencies()` can be nested, and in
    that case a new `Operation` will have control dependencies on the union
    of `control_inputs` from all active contexts.

    ```python
    with g.control_dependencies([a, b]):
      # Ops constructed here run after `a` and `b`.
      with g.control_dependencies([c, d]):
        # Ops constructed here run after `a`, `b`, `c`, and `d`.
    ```

    You can pass None to clear the control dependencies:

    ```python
    with g.control_dependencies([a, b]):
      # Ops constructed here run after `a` and `b`.
      with g.control_dependencies(None):
        # Ops constructed here run normally, not waiting for either `a` or `b`.
        with g.control_dependencies([c, d]):
          # Ops constructed here run after `c` and `d`, also not waiting
          # for either `a` or `b`.
    ```

    *N.B.* The control dependencies context applies *only* to ops that
    are constructed within the context. Merely using an op or tensor
    in the context does not add a control dependency. The following
    example illustrates this point:

    ```python
    # WRONG
    def my_func(pred, tensor):
      t = tf.matmul(tensor, tensor)
      with tf.control_dependencies([pred]):
        # The matmul op is created outside the context, so no control
        # dependency will be added.
        return t

    # RIGHT
    def my_func(pred, tensor):
      with tf.control_dependencies([pred]):
        # The matmul op is created in the context, so a control dependency
        # will be added.
        return tf.matmul(tensor, tensor)
    ```