code: def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
      if self.train_dataset is None or not has_length(
          self.train_dataset
      ):
          return None
  
      if self.args.group_by_modality_length:
          lengths = self.train_dataset.modality_lengths
          return LengthGroupedSampler(
              self.args.train_batch_size,
              world_size=self.args.world_size,  # * self.args.gradient_accumulation_steps, # Keep raw setting
              lengths=lengths,
              group_by_modality=True,
          )
      else:
          return super()._get_train_sampler()
  
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 1 for calling the LLaVATrainer obj's underlying member method code with example usage: obj._get_train_sampler() and returns: None, LengthGroupedSampler, super