code: def get_length_grouped_indices(
      lengths, batch_size, world_size, generator=None, merge=True
  ):
      # We need to use torch for the random part as a distributed sampler will set the random seed for torch.
      indices = torch.randperm(len(lengths), generator=generator)
      megabatch_size = world_size * batch_size
      megabatches = [
          indices[i : i + megabatch_size].tolist()
          for i in range(0, len(lengths), megabatch_size)
      ]
      megabatches = [
          sorted(megabatch, key=lambda i: lengths[i], reverse=True)
          for megabatch in megabatches
      ]
      megabatches = [
          split_to_even_chunks(megabatch, lengths, world_size)
          for megabatch in megabatches
      ]
  
      return [
          i
          for megabatch in megabatches
          for batch in megabatch
          for i in batch
      ]
  
imports: import os
  import torch
  import torch.nn
  from fairscale.optim import OSS
  from torch.utils.data import Sampler
  from transformers import Trainer
  from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
  from transformers.trainer_pt_utils import get_parameter_names
  from transformers.trainer_utils import ShardedDDPOption, has_length
  from transformers.utils import is_sagemaker_mp_enabled, logging
  from typing import List, Optional, Union, Dict, Any
label_desc: Use image node_id 5 for calling a global function with example usage: get_length_grouped_indices(lengths, batch_size, world_size, generator, merge) and returns: unknown