code: def prep_a_data_parallel_model(self, model, dataset, is_train):
      if model is None:
          return
  
      log.info("in prep_a_data_parallel_model")
  
      param_update = (
          self.gen_param_update_builder_fun(model, dataset, is_train)
          if self.gen_param_update_builder_fun is not None
          else None
      )
      log.info("in prep_a_data_parallel_model param_update done ")
  
      optimizer = (
          self.gen_optimizer_fun(model, dataset, is_train)
          if self.gen_optimizer_fun is not None
          else None
      )
      log.info("in prep_a_data_parallel_model optimizer done ")
  
      max_ops = self.opts["model_param"][
          "max_concurrent_distributed_ops"
      ]
      data_parallel_model.Parallelize(
          model,
          input_builder_fun=self.gen_input_builder_fun(
              model, dataset, is_train
          ),
          forward_pass_builder_fun=self.gen_forward_pass_builder_fun(
              model, dataset, is_train
          ),
          param_update_builder_fun=param_update,
          optimizer_builder_fun=optimizer,
          devices=self.xpus,
          rendezvous=self.gen_rendezvous_ctx(model, dataset, is_train),
          broadcast_computed_params=False,
          optimize_gradient_memory=self.opts["model_param"]["memonger"],
          use_nccl=self.opts["model_param"]["cuda_nccl"],
          max_concurrent_distributed_ops=max_ops,
          cpu_device=(self.opts["distributed"]["device"] == "cpu"),
          # "shared model" will only keep model parameters for cpu_0 or gpu_0
          # will cause issue when initialize each gpu_0, gpu_1, gpu_2 ...
          # shared_model=(self.opts['distributed']['device'] == 'cpu'),
          combine_spatial_bn=self.opts["model_param"][
              "combine_spatial_bn"
          ],
      )
      log.info("in prep_a_data_parallel_model Parallelize done ")
  
      # log.info("Current blobs in workspace: {}".format(workspace.Blobs()))
  
      workspace.RunNetOnce(model.param_init_net)
      log.info("in prep_a_data_parallel_model RunNetOnce done ")
  
      # for op in model.net.Proto().op:
      #     log.info('op type engine {} {}'.format(op.type, op.engine))
  
      log.info("model.net.Proto() {}".format(model.net.Proto()))
  
      workspace.CreateNet(model.net)
  
      # for op in model.net.Proto().op:
      #     log.info('after CreateNet op type engine {} {}'.
      #         format(op.type, op.engine))
  
      log.info("in prep_a_data_parallel_model CreateNet done ")
  
imports: from abc import abstractmethod
  from caffe2.python import workspace
  from caffe2.python import timeout_guard
  from caffe2.python import data_parallel_model
  from .None import checkpoint
  from .None import ModuleRegister
  from .None import module_map
  import os
  import inspect
  import time
  import logging
label_desc: Use image node_id 33 for calling the AnyExpTrainer obj's underlying member method code with example usage: obj.prep_a_data_parallel_model(model, dataset, is_train) without return types