docstr: In the forward function we accept a Variable of input data and we must return
  a Variable of output data. We can use Modules defined in the constructor as
  well as arbitrary operators on Variables.
code: def forward(self, x):
      """
      In the forward function we accept a Variable of input data and we must return
      a Variable of output data. We can use Modules defined in the constructor as
      well as arbitrary operators on Variables.
      """
      h_relu = self.linear1(x).clamp(min=0)
      y_pred = self.linear1(h_relu)
      return y_pred
  
file_path: DeepSpeed/tests/accelerator/test_ds_init.py
imports: import os
  import torch
  import deepspeed
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 2 for calling the OneLayerNet obj's underlying member method code with example usage: obj.forward(x) and returns: y_pred