docstr: A method that implements the training algorithm.
  Args:
      model (nn.Module): neural network model
      data (list): training examples
docstr_tok: ["A","method","that","implements","the","training","algorithm",".","Args",":","model","(","nn.Module",")",":","neural","network","model","data","(","list",")",":","training","examples"]
code: def train(self, model, data):
      r"""
      A method that implements the training algorithm.
      Args:
          model (nn.Module): neural network model
          data (list): training examples
      """
      model = model.cuda(self.rank)
      data = self.preprocess_data(self.rank, data)
      criterion = self.create_criterion(self.rank)
      ddp_model, hook_state = self.create_ddp_model(
          self,
          self.rank,
          model,
          self.process_group,
          self.hook_state_class,
          self.hook,
      )
      optimizer = torch.optim.SGD(ddp_model.parameters(), 1e-4)
  
      for epoch in range(self.epochs):
          if epoch % 5 == 0 and self.rank == 0:
              print(f"train epoch={epoch}")
          for index, batch in enumerate(data):
              self.iteration_step(
                  self,
                  ddp_model,
                  criterion,
                  optimizer,
                  hook_state,
                  epoch,
                  index,
                  batch,
              )
      torch.cuda.synchronize(self.rank)
  
imports: import functools
  import time
  from abc import ABC, abstractmethod
  import torch
  from metrics.MetricsLogger import MetricsLogger
label_desc: Use image node_id 3 for calling the DdpTrainer obj's underlying member method code with example usage: obj.train(model, data) without return types