class_docstr: The pipeline parallelism for Pipe.
class_docstr_tok: ["The","pipeline","parallelism","for","Pipe","."]
code: def __init__(
      self,
      partitions: List[nn.Sequential],
      devices: List[torch.device],
      copy_streams: List[List[AbstractStream]],
      skip_layout: SkipLayout,
      checkpoint_stop: int,
  ) -> None:
      self.partitions = partitions
      self.devices = devices
      self.copy_streams = copy_streams
      self.skip_layout = skip_layout
      self.checkpoint_stop = checkpoint_stop
      (self.in_queues, self.out_queues) = create_workers(devices)
  
imports: from queue import Queue
  from types import TracebackType
  from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Type, Union, cast
  import torch
  from torch import Tensor, nn
  from torch.autograd.profiler import record_function
  from .checkpoint import Checkpointing
  from .copy import Copy, Wait
  from .dependency import fork, join
  from .microbatch import Batch
  from .skip.layout import SkipLayout
  from .skip.tracker import SkipTrackerThroughPotals, use_skip_tracker
  from .stream import AbstractStream, current_stream, use_device
  from .worker import Task, create_workers, join_workers
label_desc: Use image node_id 1 to create a new Pipeline object  with example: obj = Pipeline(partitions, devices, copy_streams, skip_layout, checkpoint_stop)