code: def _ring_send_recv_aggregate(
      grad_in_tensor, d1, d2, left, right, rank, size
  ):
      # dist comms and aggregate gradients for edge pixels
      send_to_right = grad_in_tensor[:, :, :, -d2:].contiguous()
      send_to_left = grad_in_tensor[:, :, :, :d1].contiguous()
      recv_from_right = torch.zeros_like(send_to_left)
      recv_from_left = torch.zeros_like(send_to_right)
  
      send_op_right = dist.P2POp(dist.isend, send_to_right, right)
      send_op_left = dist.P2POp(dist.isend, send_to_left, left)
      recv_op_right = dist.P2POp(dist.irecv, recv_from_right, right)
      recv_op_left = dist.P2POp(dist.irecv, recv_from_left, left)
  
      reqs = dist.batch_isend_irecv(
          [send_op_right, send_op_left, recv_op_left, recv_op_right]
      )
      for req in reqs:
          req.wait()
  
      if rank == 0:
          grad_in_tensor = grad_in_tensor[:, :, :, :-d2]
          grad_in_tensor[:, :, :, -d1:] = torch.add(
              grad_in_tensor[:, :, :, -d1:], recv_from_right
          )
      elif rank == size - 1:
          grad_in_tensor = grad_in_tensor[:, :, :, d1:]
          grad_in_tensor[:, :, :, :d2] = torch.add(
              grad_in_tensor[:, :, :, :d2], recv_from_left
          )
      else:
          grad_in_tensor = grad_in_tensor[:, :, :, d1:-d2]
          grad_in_tensor[:, :, :, -d1:] = torch.add(
              grad_in_tensor[:, :, :, -d1:], recv_from_right
          )
          grad_in_tensor[:, :, :, :d2] = torch.add(
              grad_in_tensor[:, :, :, :d2], recv_from_left
          )
  
file_path: pytorch/torch/distributed/_tensor/tp_conv.py
imports: from typing import cast, Dict, List, Tuple
  import torch
  import torch.distributed
  import torch.distributed._tensor.api
label_desc: Use image node_id 4 for calling a global function with example usage: _ring_send_recv_aggregate(grad_in_tensor, d1, d2, left, right, rank, size) without return types