code: def _create_model(fsdp_config, compute_cycles, has_params: bool):
      with enable_wrap(wrapper_cls=FSDP, **fsdp_config):
          model = wrap(
              nn.Sequential(
                  wrap(Layer(compute_cycles, has_params)),
                  wrap(Layer(compute_cycles, has_params)),
                  wrap(Layer(compute_cycles, has_params)),
                  wrap(Layer(compute_cycles, has_params)),
              )
          ).cuda()
      return model
  
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 for calling a global function with example usage: _create_model(fsdp_config, compute_cycles, has_params) and returns: model