code: def event_loop_tail_across_minibatches(
      self,
      lm_dataloader: DataLoader,
      criterion: nn.Module,
      optimizer: Optimizer,
      transform_logger_object: Any,
  ) -> None:
      # handles one epoch
  
      cur_rank = self.group.rank()
      N = len(get_pipeline_parallel_ranks())
      num_batches = len(lm_dataloader)
      lm_iter = enumerate(lm_dataloader)
      # last partition -> one forward / one backward -> no warmup
      count = 0
      num_gradients = 0
      activations = dict()
  
      log_interval = 1
      word_counter = 0
      total_loss = 0
  
      while True:
          try:
              start_time = time.time()
              microbatch_index, cur_batch = next(lm_iter)
              reqd_target = transform_logger_object.transform_target(
                  cur_batch
              ).to(self.input_device)
  
              # one forward
              message = self.transport.recv_message_header(
                  EVENT_LOOP_ACTIVATIONS_QUEUE
              )
              args: AsyncMessageBody = message.args
              assert args.microbatch_index == count
              batch = self.get_batch_from_message(
                  message, EVENT_LOOP_GRADIENTS_QUEUE
              )
  
              if self.weight_prediction:
                  optimizer.update_weight_using_future_predictions(
                      cur_rank, N, count, self.chunks, forward=True
                  )
              task = create_task_without_skip_trackers(
                  self.checkpoint_stop,
                  args.microbatch_index,
                  self.group.rank(),
                  batch,
                  self.partitions[0].module,
              )
              output = task.compute()
              activations[args.microbatch_index] = output
              task.finalize(output)
              # one backward
              if self.weight_prediction:
                  optimizer.update_weight_using_future_predictions(
                      cur_rank,
                      N,
                      num_gradients,
                      self.chunks,
                      forward=False,
                  )
  
              output_tensor = (
                  transform_logger_object.transform_output_before_loss(
                      output.tensor
                  )
              )
              loss = criterion(output_tensor, reqd_target)
              loss.backward()
              count += 1
              num_gradients += 1
  
              if self.perform_optimizer_step(optimizer, num_gradients):
                  optimizer.step()
                  optimizer.zero_grad()
                  transform_logger_object.check_and_save_weights(
                      num_gradients
                  )
  
              transform_logger_object.log_loss(cur_batch, loss, count)
              del loss
              del activations[args.microbatch_index]
          except StopIteration:
              break
  
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 7 for calling the AsyncAMPnetEventLoop obj's underlying member method code with example usage: obj.event_loop_tail_across_minibatches(lm_dataloader, criterion, optimizer, transform_logger_object) without return types