code: def event_loop_trunk_forward_helper(
      self, activations: Dict[int, Batch]
  ) -> PipeMessage:
      message = self.transport.recv_message_header(
          EVENT_LOOP_ACTIVATIONS_QUEUE
      )
      args: AsyncMessageBody = message.args
      assert args.message_type is AsyncMessageType.Activations
      batch = self.get_batch_from_message(
          message, EVENT_LOOP_GRADIENTS_QUEUE
      )
      (
          activations[args.microbatch_index],
          message,
      ) = self.async_send_inner(batch, args.microbatch_index)
      return 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 8 for calling the AsyncAMPnetEventLoop obj's underlying member method code with example usage: obj.event_loop_trunk_forward_helper(activations) and returns: message