Class NoDependency
Defined in tensorflow/python/training/checkpointable/data_structures.py.
Allows attribute assignment to Checkpointable objects with no dependency.
Example usage:
obj = Checkpointable()
obj.has_dependency = tf.Variable(0., name="dep")
obj.no_dependency = NoDependency(tf.Variable(1., name="nodep"))
assert obj.no_dependency.name == "nodep:0"
obj in this example has a dependency on the variable "dep", and both
attributes contain un-wrapped Variable objects.
NoDependency also works with tf.keras.Model, but only for checkpoint
dependencies: wrapping a Layer in NoDependency will assign the (unwrapped)
Layer to the attribute without a checkpoint dependency, but the Model will
still track the Layer (so it will appear in Model.layers, and its
variables will appear in Model.variables).
__init__
__init__(value)
Initialize self. See help(type(self)) for accurate signature.