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","."]
docstr: Return an iterable for accessing the underlying sharded variables.
code: def __iter__(self):
      """Return an iterable for accessing the underlying sharded variables."""
      return iter(self._vars)
  
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 2 for calling the TPUReplicatedVariable obj's underlying member method code with example usage: obj.__iter__() and returns: iter