code: def _train_with_multi_worker(method):
      """Decorator that handles multi worker training with distribution strategy."""
  
      def wrapper(model, **kwargs):
          def _worker_fn(_):
              callbacks = kwargs.pop("callbacks", None)
              filtered_callbacks = (
                  dist_utils.filter_distributed_callbacks(
                      callbacks, model
                  )
              )
              kwargs["callbacks"] = filtered_callbacks
              return method(model, **kwargs)
  
          return dc.run_distribute_coordinator(
              _worker_fn, model._distribution_strategy
          )
  
      return wrapper
  
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 7 for calling a global function with example usage: _train_with_multi_worker(method) and returns: wrapper, dc, method