class_docstr: Training loop for distribution strategy with multiple worker.
code: def evaluate(self, *args, **kwargs):
      return _train_with_multi_worker(
          self._single_worker_loop.evaluate
      )(*args, **kwargs)
  
imports: import numpy
  from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.distribute import input_lib
  from tensorflow.python.distribute import reduce_util
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.keras import backend
  from tensorflow.python.keras import callbacks
  from tensorflow.python.keras.distribute import distribute_coordinator_utils
  from tensorflow.python.keras.distribute import distributed_training_utils_v1
  from tensorflow.python.keras.engine import partial_batch_padding_handler
  from tensorflow.python.keras.engine import training_arrays_v1
  from tensorflow.python.keras.engine import training_utils_v1
  from tensorflow.python.keras.utils.generic_utils import Progbar
  from tensorflow.python.keras.utils.mode_keys import ModeKeys
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 3 for calling the DistributionMultiWorkerTrainingLoop obj's underlying member method code with example usage: obj.evaluate() and returns: _train_with_multi_worker