class_docstr: A replicated variable for use on TPUs.
  
  When accessed inside a tpu.replicate() context, this variable acts as if it
  is a single variable whose handle is a replicated input to the computation.
  
  Outside a tpu.replicate() context currently this object has pretty murky
  semantics, especially with respect to things such as
  * initialization
  * colocation.
  
  TODO(phawkins): merge this with the TPU DistributionStrategy code.
docstr: Makes assignments depend on the cached value, if any.
  
  This prevents undefined behavior with reads not ordered wrt writes.
  
  Yields:
    None.
docstr_tok: ["Makes","assignments","depend","on","the","cached","value",",","if","any",".","This","prevents","undefined","behavior","with","reads","not","ordered","wrt","writes",".","Yields",":","None","."]
code: def _assign_dependencies(self):
      """Makes assignments depend on the cached value, if any.
  
      This prevents undefined behavior with reads not ordered wrt writes.
  
      Yields:
        None.
      """
      if self._cached_value is not None:
          with ops.control_dependencies([self._cached_value]):
              yield
      else:
          yield
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import contextlib
  import inspect
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_conversion_registry
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import gen_resource_variable_ops
label_desc: Use image node_id 3 for calling the ReplicatedVariable obj's underlying member method code with example usage: obj._assign_dependencies() without return types