code: def event_loop_across_minibatches(
      self,
      lm_dataloader: DataLoader,
      criterion: nn.Module,
      optimizer: Optimizer,
      transform_logger_object: Any,
  ) -> None:
      activations: Dict[int, Batch] = dict()
      num_microbatch = len(lm_dataloader)
      num_activations = 0
      num_gradients = 0
  
      ranks = get_pipeline_parallel_ranks()  # for warmup phase
      N = len(ranks)
      cur_rank = torch.distributed.get_rank()
  
      # warmup phase (forward passes)
      # cur_rank worker will do (max_rank - cur_rank) forward passes
      n_warmup = ranks[-1] - cur_rank
      for _ in range(n_warmup):
          if self.weight_prediction:
              optimizer.update_weight_using_future_predictions(
                  cur_rank,
                  N,
                  num_activations,
                  self.chunks,
                  forward=True,
              )
          message = self.event_loop_trunk_forward_helper(activations)
          self.transport.send_message(message, sync=True)
          num_activations += 1
  
      # common loop for remanining items in the warmup phase and steady phase
      while num_activations < num_microbatch:
          # 1 Forward
          if self.weight_prediction:
              optimizer.update_weight_using_future_predictions(
                  cur_rank,
                  N,
                  num_activations,
                  self.chunks,
                  forward=True,
              )
          message = self.event_loop_trunk_forward_helper(activations)
  
          num_activations += 1
          # 1 Backward
          if self.weight_prediction:
              optimizer.update_weight_using_future_predictions(
                  cur_rank, N, num_gradients, self.chunks, forward=False
              )
          self.event_loop_trunk_backward_helper(activations)
          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
              )
  
          self.transport.send_message(message, sync=True)
  
      # remaining backwards
      remaining = len(activations)
      for _ in range(remaining):
          if self.weight_prediction:
              optimizer.update_weight_using_future_predictions(
                  cur_rank, N, num_gradients, self.chunks, forward=False
              )
          self.event_loop_trunk_backward_helper(activations)
          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
              )
  
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 10 for calling the AsyncAMPnetEventLoop obj's underlying member method code with example usage: obj.event_loop_across_minibatches(lm_dataloader, criterion, optimizer, transform_logger_object) without return types