code: def __init__(self, context: det_ds.DeepSpeedTrialContext):
      self.context = context
      self.hparams = attrdict.AttrDict(context.get_hparams())
      self.ds_config = attrdict.AttrDict(self.hparams.deepspeed_config)
      model = torch.nn.Linear(1, 1)
      model = deepspeed.PipelineModule(
          layers=[model],
          loss_fn=torch.nn.MSELoss(),
          num_stages=1,
      )
      self.model, _, _, _ = deepspeed.initialize(
          model=model,
          config=self.ds_config,
          model_parameters=[
              p for p in model.parameters() if p.requires_grad
          ],
      )
      self.model = self.context.wrap_model_engine(self.model)
      self.context.set_mpu(det_ds.make_deepspeed_mpu(self.model.mpu))
  
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 LinearPipelineEngineTrial object from inherited base classes: LinearDeepSpeedTrial with example: obj = LinearPipelineEngineTrial(context)