code: def __init__(
      self,
      partitions: List[ModuleWrapper],
      group: ProcessGroup,
      transport: Transport,
      min_update_interval: int,
      weight_prediction: bool,
      checkpoint_stop: int,
      input_device: Union[None, int, str, torch.device],
      chunks: int,
  ):
      self.partitions = partitions
      self.group = group
      self.transport = transport
      self.min_update_interval = min_update_interval
      self.weight_prediction = weight_prediction
      self.checkpoint_stop = checkpoint_stop
      self.input_device = input_device
      self.chunks = chunks
  
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 1 to create a new AsyncAMPnetEventLoop object  with example: obj = AsyncAMPnetEventLoop(partitions, group, transport, min_update_interval, weight_prediction, checkpoint_stop, input_device, chunks)