code: def time_model():
      """Times model."""
      # Setup training/testing environment
      setup_env()
      # Construct the model and loss_fun
      model, _ = setup_model()
      loss_fun = builders.build_loss_fun().cuda()
      # Compute model and loader timings
      benchmark.compute_time_model(model, loss_fun)
  
imports: import contextlib
  import os
  import random
  from copy import deepcopy
  import numpy
  import pycls.core.benchmark
  import pycls.core.builders
  import pycls.core.checkpoint
  import pycls.core.config
  import pycls.core.distributed
  import pycls.core.logging
  import pycls.core.meters
  import pycls.core.net
  import pycls.core.optimizer
  import pycls.datasets.loader
  import torch
  import torch.cuda.amp
  from fairscale.nn import auto_wrap, config_auto_wrap_policy, enable_wrap
  from fairscale.nn.data_parallel import FullyShardedDataParallel
  from fairscale.optim.grad_scaler import ShardedGradScaler
  from pycls.core.config import cfg
  from pycls.core.io import cache_url, pathmgr
label_desc: Use image node_id 9 for calling a global function with example usage: time_model() without return types