code: def sparse_all_to_all_push(idx, value, partition):
      """Perform an all-to-all-v operation, where by all processors send out
      a set of indices and corresponding values. Indices and values,
      corresponding to the current process, will copied into the output
      arrays.
  
      Note: This method requires 'torch.distributed.get_backend() == "nccl"'.
  
      Parameters
      ----------
      idx : torch.Tensor
          The 1D set of indices to send to other processors.
      value : torch.Tensor
          The multi-dimension set of values to send to other processors.
          The first dimension must match that of `idx`.
      partition : NDArrayPartition
          The object containing information for assigning indices to
          processors.
  
      Returns
      -------
      torch.Tensor
          The 1D tensor of the recieved indices.
      torch.Tensor
          The set of recieved values.
  
      Examples
      --------
  
      To perform a sparse_all_to_all_push(), a partition object must be
      provided. A partition of a homgeonous graph, where the vertices are
      striped across processes can be generated via:
  
      >>> from dgl.partition import NDArrayPartition
      >>> part = NDArrayPartition(g.num_nodes(), world_size, mode='remainder')
  
      With this partition, each processor can send values to be associatd
      with vertices in the graph. So if we have an array `global_idxs` of all of
      the neighbors updated during mini-batch processing, and an array
      `global_values` containing the new values associated with the neighbors,
      we communicate them to the own processes via:
  
      >>> my_idxs, my_values = nccl.sparse_all_to_all_push(global_idxs, global_values, part)
  
      This communication pattern is common when communicating gradient
      updates for node embeddings.
  
      Indices the current process owns, do not need to treated specially,
      as internally they will be copied to the output array. If we have a
      set of indices in process 0 '[0, 3, 8, 9, 10]` and for process 1
      '[0, 2, 4, 5, 8, 8, 9]'. Using a remainder partition will result
      indices for processe 0 of '[0, 8, 10, 0, 2, 4, 8, 8]', and for
      process 1 of '[3, 9, 5, 9]'.
      """
      if not dist.is_initialized() or dist.get_world_size() == 1:
          return idx, value
      assert (
          dist.get_backend() == "nccl"
      ), "requires NCCL backend to communicate CUDA tensors."
  
      perm, send_splits = partition.generate_permutation(idx)
      perm = perm.long()
  
      # Get receive splits.
      recv_splits = torch.empty_like(send_splits)
      dist.all_to_all_single(recv_splits, send_splits)
  
      # Use pinned memory to speedup D2H copy.
      recv_splits = recv_splits.to("cpu", non_blocking=True)
      send_splits = send_splits.to("cpu", non_blocking=True)
      send_idx = idx[perm]
      send_value = value[perm]
      # Wait D2H copy finish.
      torch.cuda.current_stream().synchronize()
      recv_sum = recv_splits.sum()
      recv_splits = recv_splits.tolist()
      send_splits = send_splits.tolist()
  
      # Send idx.
      recv_idx = torch.empty(
          (recv_sum,), dtype=idx.dtype, device=idx.device
      )
      dist.all_to_all_single(
          recv_idx, send_idx, recv_splits, send_splits
      )
  
      # Send value.
      recv_value = torch.empty(
          (recv_sum, *value.shape[1:]),
          dtype=value.dtype,
          device=value.device,
      )
      dist.all_to_all_single(
          recv_value, send_value, recv_splits, send_splits
      )
  
      return recv_idx, recv_value
  
label_desc: Use image node_id 1 for calling a global function with example usage: sparse_all_to_all_push(idx, value, partition) and returns: recv_idx,  recv_value, idx,  value