code: def __init__(self, context: det_ds.DeepSpeedTrialContext):
      self.context = context
      self.hparams = attrdict.AttrDict(context.get_hparams())
      if (
          self.hparams.test_manual_init_distributed
          or self.hparams.test_fail_manual_init_distributed
      ):
          assert (
              not torch.distributed.is_initialized()
          ), "distributed backend should not be initialized"
      if (
          self.hparams.test_manual_init_distributed
          and not self.hparams.test_fail_manual_init_distributed
      ):
          deepspeed.init_distributed(auto_mpi_discovery=False)
      if (
          self.hparams.test_manual_grad_acc
          or self.hparams.test_fail_manual_grad_acc
      ):
          self.context.disable_auto_grad_accumulation()
      if self.hparams.test_manual_dataloader:
          self.context.disable_dataset_reproducibility_checks()
      self.ds_config = attrdict.AttrDict(self.hparams.deepspeed_config)
      model = torch.nn.Linear(1, 1)
      self.model, optimizer, _, _ = deepspeed.initialize(
          model=model,
          config=self.ds_config,
          model_parameters=model.parameters(),
          dist_init_required=False,
      )
      self.model = self.context.wrap_model_engine(self.model)
      self.loss = torch.nn.MSELoss()
      self.reducer = None
      if self.hparams.test_custom_reducer:
          self.reducer = self.context.wrap_reducer(
              lambda x: np.mean(x) * 2, name="loss_2x"
          )
  
imports: from typing import Any, Dict, Iterator, Optional, Tuple, Union
  import attrdict
  import deepspeed
  import deepspeed.runtime.dataloader
  import numpy
  import torch
  import determined.pytorch.deepspeed
  import tests.experiment.fixtures.pytorch_counter_callback
  from determined import pytorch
label_desc: Use image node_id 1 to create a new LinearDeepSpeedTrial object from inherited base classes: det_ds with example: obj = LinearDeepSpeedTrial(context)