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: import argparse
  import math
  import os
  import time
  import torch
  import torch.nn
  from benchmark_dataset import BenchmarkLMDataset, collate_sentences_lm
  from torch.distributed import rpc
  from torch.distributed.pipeline.sync import Pipe
  from torch.distributed.pipeline.sync.utils import partition_model
  from torch.optim import Adam
  from torch.utils.data import DataLoader
label_desc: Use image node_id 5 for calling a global function with example usage: generate_balance(num_devices, num_layers) and returns: balance