old_contents: # -*- coding:utf-8 -*-
  # Author: hankcs
  # Date: 2019-06-13 18:05
  import hanlp.common
  import hanlp.components
  import hanlp.pretrained
  import hanlp.utils
  from hanlp.version import __version__
  
  hanlp.utils.ls_resource_in_module(hanlp.pretrained)
  
  
  def load(save_dir: str, verbose=None, **kwargs) -> hanlp.common.component.Component:
      """Load pretrained component from an identifier.
  
      Args:
        save_dir (str): The identifier to the saved component. It could be a remote URL or a local path.
        verbose: ``True`` to print loading progress.
        **kwargs: Arguments passed to :func:`hanlp.common.torch_component.TorchComponent.load`
  
      ``devices`` is a useful arguments to specify the GPU devices component will use.
  
      Examples::
  
          import hanlp
          # Load component onto the 0-th GPU.
          hanlp.load(..., devices=0)
          # Load component onto the 0-th and 1-st GPU using data parallelization.
          hanlp.load(..., devices=[0,1])
  
      .. Note::
          A component can have dependencies on other components or resources, which will be recursively loaded. So it's
          common to see multiple downloading per single load.
  
      Returns:
        hanlp.common.component.Component: A pretrained component.
  
      """
      save_dir = hanlp.pretrained.ALL.get(save_dir, save_dir)
      from hanlp.utils.component_util import load_from_meta_file
      if verbose is None:
          from hanlp_common.constant import HANLP_VERBOSE
          verbose = HANLP_VERBOSE
      return load_from_meta_file(save_dir, 'meta.json', verbose=verbose, **kwargs)
  
  
  def pipeline(*pipes) -> hanlp.components.pipeline.Pipeline:
      """Creates a pipeline of components. It's made for bundling KerasComponents. For TorchComponent, use
      :class:`~hanlp.components.mtl.multi_task_learning.MultiTaskLearning` instead.
  
      Args:
        *pipes: Components if pre-defined any.
  
      Returns:
        A pipeline, which is list of components in order.
  
      """
      return hanlp.components.pipeline.Pipeline(*pipes)
  
new_contents: # -*- coding:utf-8 -*-
  # Author: hankcs
  # Date: 2019-06-13 18:05
  import hanlp.common
  import hanlp.components
  import hanlp.pretrained
  import hanlp.utils
  from hanlp.version import __version__
  
  hanlp.utils.ls_resource_in_module(hanlp.pretrained)
  
  
  def load(save_dir: str, verbose=None, **kwargs) -> hanlp.common.component.Component:
      """Load pretrained component from an identifier.
  
      Args:
        save_dir (str): The identifier to the saved component. It could be a remote URL or a local path.
        verbose: ``True`` to print loading progress.
        **kwargs: Arguments passed to :func:`hanlp.common.torch_component.TorchComponent.load`, e.g.,
          ``devices`` is a useful argument to specify which GPU devices a PyTorch component will use.
  
      Examples::
  
          import hanlp
          # Load component onto the 0-th GPU.
          hanlp.load(..., devices=0)
          # Load component onto the 0-th and 1-st GPU using data parallelization.
          hanlp.load(..., devices=[0,1])
  
      .. Note::
          A component can have dependencies on other components or resources, which will be recursively loaded. So it's
          common to see multiple downloading messages per single load.
  
      Returns:
        hanlp.common.component.Component: A pretrained component.
  
      """
      save_dir = hanlp.pretrained.ALL.get(save_dir, save_dir)
      from hanlp.utils.component_util import load_from_meta_file
      if verbose is None:
          from hanlp_common.constant import HANLP_VERBOSE
          verbose = HANLP_VERBOSE
      return load_from_meta_file(save_dir, 'meta.json', verbose=verbose, **kwargs)
  
  
  def pipeline(*pipes) -> hanlp.components.pipeline.Pipeline:
      """Creates a pipeline of components. It's made for bundling `KerasComponents`. For `TorchComponent`, use
      :class:`~hanlp.components.mtl.multi_task_learning.MultiTaskLearning` instead.
  
      Args:
        *pipes: Components if pre-defined any.
  
      Returns:
        A pipeline, which is list of components in order.
  
      """
      return hanlp.components.pipeline.Pipeline(*pipes)
  
subject: Revise documentation