class_docstr: Manages dependencies on other objects.
  
  `Trackable` objects may have dependencies: other `Trackable` objects
  which should be saved if the object declaring the dependency is saved. A
  correctly saveable program has a dependency graph such that if changing a
  global variable affects an object (e.g. changes the behavior of any of its
  methods) then there is a chain of dependencies from the influenced object to
  the variable.
  
  Dependency edges have names, and are created implicitly when a
  `Trackable` object is assigned to an attribute of another
  `Trackable` object. For example:
  
  ```
  obj = Trackable()
  obj.v = ResourceVariable(0.)
  ```
  
  The `Trackable` object `obj` now has a dependency named "v" on a
  variable.
  
  `Trackable` objects may specify `Tensor`s to be saved and restored
  directly (e.g. a `Variable` indicating how to save itself) rather than through
  dependencies on other objects. See
  `Trackable._gather_saveables_for_checkpoint` for details.
docstr: Override to allow TrackableBase to disable dependency tracking.
docstr_tok: ["Override","to","allow","TrackableBase","to","disable","dependency","tracking","."]
code: def _no_dependency(self, value):
      """Override to allow TrackableBase to disable dependency tracking."""
      return data_structures.NoDependency(value)
  
file_path: tensorflow/tensorflow/python/trackable/autotrackable.py
imports: import warnings
  from absl import logging
  from tensorflow.python.eager import def_function
  from tensorflow.python.eager import function
  from tensorflow.python.trackable import base
  from tensorflow.python.trackable import data_structures
  from tensorflow.python.types import core
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 3 for calling the AutoTrackable obj's underlying member method code with example usage: obj._no_dependency(value) and returns: data_structures