code: def __init__(self, fs_args, ds_config, task):
      self.fs_args = fs_args
      self.ds_config = ds_config
      self.task = task
      model = task.build_model(fs_args)
      self.criterion = task.build_criterion(fs_args)
      model = DsFairseqModel(model, self.criterion)
      self.prepare_model_optimizer(model)
  
imports: import math
  import torch
  from torch import nn
  import torch.distributed
  import deepspeed
  from deepspeed.utils import log_dist
  from fairseq import tasks, distributed_utils
  from fairseq.logging import metrics
  from ds_fairseq_data import BatchIterator
  from ds_fairseq_argument import gen_ds_fairseq_arg
label_desc: Use image node_id 1 to create a new DsFairseqTrainer object from inherited base classes: object with example: obj = DsFairseqTrainer(fs_args, ds_config, task)