code: def step(self, closure=None):
      loss_1 = self.adam.step(closure)
      loss_2 = self.adam_sparse.step(closure)
  
      if loss_1 is not None and loss_2 is not None:
          return loss_1 + loss_2
      return loss_1 or loss_2
  
file_path: DeepSpeed/tests/unit/runtime/sparse_tensor/test_averaging_sparse_gradients.py
imports: import torch
  import deepspeed
  from unit.common import DistributedTest
  from unit.util import skip_on_arch
label_desc: Use image node_id 2 for calling the Adam obj's underlying member method code with example usage: obj.step(closure) and returns: unknown, unknown