code: def forward(self, input):
      act1 = self.linear1(input)
      if self.output_type is dict:
          return {"loss": act1.sum()}
      if self.output_type is torch.tensor:
          return act1.sum()
  
imports: import os
  from types import SimpleNamespace
  import torch
  import pytest
  import deepspeed
  from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus, partitioned_param_data_shape
  import deepspeed.comm
  from unit.common import DistributedTest, get_master_port
label_desc: Use image node_id 2 for calling the ModelContainerVariableOutputType obj's underlying member method code with example usage: obj.forward(input) and returns: dict, act1