code: def event_loop_head_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())  # for warmup phase
      activations = dict()
      count = 0
      num_gradients = 0
      lm_iter = iter(lm_dataloader)
  
      # filling the pipeline: warmup  -> all N - 1 forward passes
      while True:
          try:
              cur_batch = next(lm_iter)
              reqd_input = transform_logger_object.transform_input(
                  cur_batch
              ).to(self.input_device)
              batch = Batch(reqd_input, count)
              if self.weight_prediction:
                  optimizer.update_weight_using_future_predictions(
                      cur_rank, N, count, self.chunks, forward=True
                  )
              activations[count], message = self.async_send_inner(
                  batch, count
              )
              self.transport.send_message(message, sync=True)
              count += 1
              if count == N - 1:
                  break
          except StopIteration:
              break
  
      # steady state
      while True:
          try:
              # 1 forward pass
              cur_batch = next(lm_iter)
              reqd_input = transform_logger_object.transform_input(
                  cur_batch
              ).to(self.input_device)
              batch = Batch(reqd_input, count)
              if self.weight_prediction:
                  optimizer.update_weight_using_future_predictions(
                      cur_rank, N, count, self.chunks, forward=True
                  )
              (
                  activations[count],
                  forward_message,
              ) = self.async_send_inner(batch, count)
              count += 1
  
              # 1 backward pass
              message = self.transport.recv_message_header(
                  EVENT_LOOP_GRADIENTS_QUEUE
              )
              args: AsyncMessageBody = message.args
              assert args.message_type is AsyncMessageType.Gradients
              if self.weight_prediction:
                  optimizer.update_weight_using_future_predictions(
                      cur_rank,
                      N,
                      num_gradients,
                      self.chunks,
                      forward=False,
                  )
              self.async_grad_inner(message, activations)
  
              # Send after grad
              self.transport.send_message(forward_message, sync=True)
  
              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
                  )
  
          except StopIteration:
              break
  
      # remaining items for backward
      remaining_items = len(activations)
      for _ in range(remaining_items):
          message = self.transport.recv_message_header(
              EVENT_LOOP_GRADIENTS_QUEUE
          )
          args = message.args
          assert args.message_type is AsyncMessageType.Gradients
          if self.weight_prediction:
              optimizer.update_weight_using_future_predictions(
                  cur_rank, N, num_gradients, self.chunks, forward=False
              )
          self.async_grad_inner(message, 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 6 for calling the AsyncAMPnetEventLoop obj's underlying member method code with example usage: obj.event_loop_head_across_minibatches(lm_dataloader, criterion, optimizer, transform_logger_object) without return types