code: def backward(ctx, grad_output):
      group, rank, world_size = get_group_world_size_rank()
  
      assert grad_output.shape[0] % world_size == 0
      dim_size = grad_output.shape[0] // world_size
      output_list = torch.split(grad_output, dim_size, dim=0)
  
      # get chunk from this rank
      output = output_list[rank].contiguous()
      return output
  
imports: from functools import partial
  import math
  import torch
  import torch.distributed
  import torch.nn.functional
  from megatron import get_args
  from megatron import print_rank_0
  from megatron import get_timers
  from megatron.core import mpu
  from megatron.core.enums import ModelType
  from megatron.data.biencoder_dataset_utils import get_ict_batch
  from megatron.data.dataset_utils import build_train_valid_test_datasets
  from megatron.model.biencoder_model import biencoder_model_provider
  from megatron.training import pretrain
  from megatron.utils import average_losses_across_data_parallel_group
label_desc: Use image node_id 2 for calling the AllgatherFromDataParallelRegion obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: output