code: def async_send_inner(
      self, batch: Batch, index: int
  ) -> Tuple[Batch, PipeMessage]:
      task = create_task_without_skip_trackers(
          self.checkpoint_stop,
          index,
          self.group.rank(),
          batch,
          self.partitions[0].module,
      )
      result = task.compute()
      task.finalize(result)
  
      ranks = get_pipeline_parallel_ranks()
      this_rank = torch.distributed.get_rank()
  
      body = AsyncMessageBody(
          AsyncMessageType.Activations,
          index,
          Location(this_rank, 0),
          Location(ranks[ranks.index(this_rank) + 1], 0),
          0,
      )
      message = PipeMessage(
          this_rank,
          ranks[ranks.index(this_rank) + 1],
          queue_name=EVENT_LOOP_ACTIVATIONS_QUEUE,
          args=body,
          tensors=tuple([*result]),
      )
      return result, message
  
imports: import time
  from typing import Any, Dict, List, Tuple, Union
  import torch
  from torch import nn
  from torch.autograd.profiler import record_function
  from torch.distributed import ProcessGroup
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  from fairscale.nn.model_parallel import get_pipeline_parallel_ranks
  from fairscale.nn.pipe.async_schedule import AsyncMessageBody, AsyncMessageType, AsyncRecvOperator, Location, ModuleWrapper
  from fairscale.nn.pipe.checkpoint import Checkpointing
  from fairscale.nn.pipe.messages import Transport
  from fairscale.nn.pipe.microbatch import Batch
  from fairscale.nn.pipe.types import EVENT_LOOP_ACTIVATIONS_QUEUE, EVENT_LOOP_GRADIENTS_QUEUE, PipeMessage, TensorOrTensors
  from fairscale.nn.pipe.worker import Task
label_desc: Use image node_id 3 for calling the AsyncAMPnetEventLoop obj's underlying member method code with example usage: obj.async_send_inner(batch, index) and returns: result,  message