code: def forward(self, x):
      # Get 2 events.
      self.e1 = Event(enable_timing=True)
      self.e2 = Event(enable_timing=True)
  
      # Record the fake forward compute time.
      self.e1.record()
      if self.sleep_cycles > 0:
          torch.cuda._sleep(self.sleep_cycles)
      if self.optional_param is not None:
          x = (
              x + self.optional_param
          )  # force the param to be part of the graph
      self.e2.record()
      return x
  
imports: from statistics import mean
  import time
  from unittest.mock import patch
  import pytest
  import torch
  from torch.cuda import Event
  import torch.multiprocessing
  import torch.nn
  from fairscale.fair_dev.testing.testing import dist_init, get_cycles_per_ms, skip_if_single_gpu, teardown, temp_files_ctx
  from fairscale.internal import torch_version
  from fairscale.nn import enable_wrap, wrap
  from fairscale.nn.data_parallel import FullyShardedDataParallel
label_desc: Use image node_id 2 for calling the Layer obj's underlying member method code with example usage: obj.forward(x) and returns: x