code: def generate_balance(num_devices, num_layers):
      balance = []
      layers_assigned = 0
      for i in range(num_devices):
          x = (num_layers - layers_assigned) / (num_devices - i)
          if x.is_integer():
              balance.append(int(x))
              layers_assigned += x
          else:
              balance.append(math.ceil(x))
              layers_assigned += math.ceil(x)
      return balance
  
imports: from collections import defaultdict
  import gc
  import logging
  import math
  import time
  import torch
  import torch.distributed
  from torch.distributed import rpc
  from torch.nn.parallel import DistributedDataParallel
  import utils
  from benchmarks.golden_configs.lm_wikitext2 import Pipe
  from fairscale.fair_dev.testing.testing import dist_init
  from fairscale.nn import Pipe
  from fairscale.nn.model_parallel import initialize_model_parallel
label_desc: Use image node_id 10 for calling a global function with example usage: generate_balance(num_devices, num_layers) and returns: balance