code: def create_distributed_learner(self, mode, config):
      local_learner = C.sgd(
          self.z.parameters,
          C.learning_parameter_schedule_per_sample(0.01),
      )
      try:
          if mode == "data_parallel":
              if config is None:
                  config = DataParallelConfig(
                      num_quantization_bits=32, distributed_after=0
                  )
              learner = C.data_parallel_distributed_learner(
                  local_learner,
                  num_quantization_bits=config.num_quantization_bits,
                  distributed_after=config.distributed_after,
              )
          elif mode == "block_momentum":
              if config is None:
                  # the default config to match data parallel SGD
                  config = BlockMomentumConfig(
                      block_momentum_as_time_constant=0,
                      block_learning_rate=1,
                      block_size=NUM_WORKERS,
                      distributed_after=0,
                  )
              learner = C.block_momentum_distributed_learner(
                  local_learner,
                  block_momentum_as_time_constant=config.block_momentum_as_time_constant,
                  block_learning_rate=config.block_learning_rate,
                  block_size=config.block_size,
                  distributed_after=config.distributed_after,
              )
          else:
              learner = local_learner
      except RuntimeError:
          learner = None
      return learner
  
imports: import argparse
  import collections
  import os
  import pickle
  import pytest
  import re
  import signal
  import subprocess
  import sys
  import time
  import numpy
  import cntk
label_desc: Use image node_id 4 for calling the SimpleTrainer obj's underlying member method code with example usage: obj.create_distributed_learner(mode, config) and returns: learner