class_docstr: Container for replicated `Variables` that are treated as a single variable.
  
  This class maintains a list of replicated variables that are stored on
  separate logic TPU devices. TF2XLA bridge accesses these variables as
  if they were a single variable.
class_docstr_tok: ["Container","for","replicated","`","Variables","`","that","are","treated","as","a","single","variable",".","This","class","maintains","a","list","of","replicated","variables","that","are","stored","on","separate","logic","TPU","devices",".","TF2XLA","bridge","accesses","these","variables","as","if","they","were","a","single","variable","."]
code: def assign_sub(
      self, value, use_locking=False, name=None, read_value=True
  ):
      if (
          tpu_util.enclosing_tpu_context() is None
          or context.executing_eagerly()
      ):
          assign_sub_fn = lambda var, *a, **ka: var.assign_sub(*a, **ka)
          return self._update(
              assign_sub_fn,
              value=value,
              use_locking=use_locking,
              name=name,
              read_value=read_value,
          )
      else:
          return tpu_util.make_raw_assign_fn(
              gen_resource_variable_ops.assign_sub_variable_op
          )(
              self,
              value=value,
              use_locking=use_locking,
              name=name,
              read_value=read_value,
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from collections import abc
  import contextlib
  from tensorflow.python.compiler.xla.experimental import xla_sharding
  from tensorflow.python.distribute import tpu_util
  from tensorflow.python.eager import context
  from tensorflow.python.framework import config
  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
  from tensorflow.python.ops import gen_tpu_partition_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.saved_model import save_context
  from tensorflow.python.trackable import base
label_desc: Use image node_id 29 for calling the TPUReplicatedVariable obj's underlying member method code with example usage: obj.assign_sub(value, use_locking, name, read_value) and returns: self, tpu_util