code: def no_automatic_dependency_tracking_scope(obj):
      """A context that disables automatic dependency tracking when assigning attrs.
  
      Objects that inherit from Autotrackable automatically creates dependencies
      to trackable objects through attribute assignments, and wraps data structures
      (lists or dicts) with trackable classes. This scope may be used to temporarily
      disable this behavior. This works similar to the decorator
      `no_automatic_dependency_tracking`.
  
      Example usage:
      ```
      model = tf.keras.Model()
      model.arr1 = []  # Creates a ListWrapper object
      with no_automatic_dependency_tracking_scope(model):
        model.arr2 = []  # Creates a regular, untracked python list
      ```
  
      Args:
        obj: A trackable object.
  
      Yields:
        a scope in which the object doesn't track dependencies.
      """
      previous_value = getattr(obj, "_setattr_tracking", True)
      obj._setattr_tracking = False  # pylint: disable=protected-access
      try:
          yield
      finally:
          obj._setattr_tracking = previous_value
  
imports: import itertools
  import threading
  import types
  from tensorflow.python.eager import context
  from tensorflow.python.keras import backend
  from tensorflow.python.keras.engine import base_layer_utils
  from tensorflow.python.keras.utils import control_flow_util
  from tensorflow.python.keras.utils import tf_contextlib
  from tensorflow.python.keras.utils import tf_inspect
  from tensorflow.python.keras.utils.generic_utils import LazyLoader
  from tensorflow.python.util import tf_decorator
label_desc: Use image node_id 12 for calling a global function with example usage: no_automatic_dependency_tracking_scope(obj) without return types