code: def __init__(self, compute_cycles, has_params: bool):
      super().__init__()
      self.sleep_cycles = compute_cycles
      self.optional_param = None
      if has_params:
          self.optional_param = nn.Parameter(torch.rand(1))
  
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 1 to create a new Layer object from inherited base classes: nn with example: obj = Layer(compute_cycles, has_params)