code: def _predict(self, rdd: RDD):
      json_model = self.master_network.to_json()
      weights = self.master_network.get_weights()
      weights = rdd.context.broadcast(weights)
      custom_objs = self.custom_objects
  
      def _predict(model_as_json, custom_objects, data):
          model = model_from_json(model_as_json, custom_objects)
          model.set_weights(weights.value)
          data = np.array([x for x in data])
          return model.predict(data)
  
      def _predict_with_indices(model_as_json, custom_objects, data):
          model = model_from_json(model_as_json, custom_objects)
          model.set_weights(weights.value)
          data, indices = zip(*data)
          data = np.array(data)
          return zip(model.predict(data), indices)
  
      if self.num_workers and self.num_workers > 1:
          # if there are multiple workers, we need to retrieve element indices and preserve them throughout
          # the inference process, since we'll need to sort by index before returning the result, as repartitioning
          # does not preserve ordering, but the users will expect prediction results which correspond to the ordering
          # of samples they supplied.
          rdd = rdd.zipWithIndex()
          rdd = rdd.repartition(self.num_workers)
          predictions_and_indices = rdd.mapPartitions(
              partial(_predict_with_indices, json_model, custom_objs)
          )
          predictions_sorted_by_index = predictions_and_indices.sortBy(
              lambda x: x[1]
          )
          predictions = predictions_sorted_by_index.map(
              lambda x: x[0]
          ).collect()
      else:
          # if there are no workers specified or only a single worker, we don't need to worry about handling index
          # values, since there will be no shuffling
          predictions = rdd.mapPartitions(
              partial(_predict, json_model, custom_objs)
          ).collect()
  
      return predictions
  
imports: import json
  from functools import partial
  from itertools import tee
  from typing import Union
  import h5py
  import numpy
  import pyspark
  from pyspark import RDD
  from tensorflow.keras.models import load_model
  from tensorflow.keras.models import model_from_json
  from tensorflow.keras.optimizers import get
  from tensorflow.keras.optimizers import serialize, deserialize
  from .mllib import to_matrix, from_matrix, to_vector, from_vector
  from .parameter.factory import ClientServerFactory
  from .utils import lp_to_simple_rdd, to_simple_rdd
  from .utils import model_to_dict
  from .utils import subtract_params, divide_by
  from .worker import AsynchronousSparkWorker, SparkWorker
label_desc: Use image node_id 13 for calling the SparkModel obj's underlying member method code with example usage: obj._predict(rdd) and returns: predictions, model, zip