class_docstr: Device chooser for variables.
  
  When using a parameter server it will assign them in a round-robin fashion.
  When not using a parameter server it allows GPU or CPU placement.
class_docstr_tok: ["Device","chooser","for","variables",".","When","using","a","parameter","server","it","will","assign","them","in","a","round-robin","fashion",".","When","not","using","a","parameter","server","it","allows","GPU","or","CPU","placement","."]
code: def __call__(self, op):
      device_spec = tf_device.DeviceSpec(
          replica=self._replica,
          device_type=self._device_type,
          device_index=self._device_index,
      )
      if self._num_tasks > 0:
          task_id = self._next_task_id
          self._next_task_id = (
              self._next_task_id + 1
          ) % self._num_tasks
          device_spec.job = self._job_name
          device_spec.task = task_id
      return device_spec.to_string()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import re
  from .arg_scope import add_arg_scope
  from tensorflow.core.protobuf import saver_pb2
  from tensorflow.python import pywrap_tensorflow
  from tensorflow.python.framework import device
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import resource_loader
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.training import saver
  from tensorflow.python.training import training_util
  from tensorflow.python.util.deprecation import deprecated
label_desc: Use image node_id 2 for calling the VariableDeviceChooser obj's underlying member method code with example usage: obj.__call__(op) and returns: device_spec