code: def _make_augmentation_pipeline(augmentation_list):
      """Build an sklearn pipeline of augmentations from a tuple of strings.
  
      Parameters
      ----------
      augmentation_list: list of strings, A list of strings that determine the
          augmentations to apply, and in which order to apply them (the first
          string will be applied first). Possible augmentation strings are
          ['leadlag', 'ir', 'addtime', 'cumsum', 'basepoint']
  
      Returns
      -------
      sklearn.Pipeline
          The transforms, in order, as an sklearn pipeline.
  
      Examples
      --------
          augementations = ('leadlag', 'ir', 'addtime')
          _make_augmentation_pipeline(augmentations)
          # Will return
          Pipeline([
              ('leadlag', LeadLag()),
              ('ir', InvisibilityReset()),
              ('addtime', AddTime())
          ])
      """
      # Dictionary of augmentations
      AUGMENTATIONS = {
          "leadlag": _LeadLag(),
          "ir": _InvisibilityReset(),
          "addtime": _AddTime(),
          "cumsum": _CumulativeSum(),
          "basepoint": _BasePoint(),
      }
  
      # Assertions, check we have a tuple/list
      if augmentation_list is not None:
          if isinstance(augmentation_list, str):
              augmentation_list = (augmentation_list,)
          if not [
              x in list(AUGMENTATIONS.keys()) for x in augmentation_list
          ]:
              raise ValueError(
                  "augmentation_list must only contain string elements from "
                  f" {list(AUGMENTATIONS.keys())}. Found: {augmentation_list}"
              )
  
      # Setup pipeline
      if augmentation_list is not None:
          pipeline = Pipeline(
              [
                  (tfm_str, AUGMENTATIONS[tfm_str])
                  for tfm_str in augmentation_list
              ]
          )
      else:
          pipeline = None
  
      return pipeline
  
imports: import numpy
  from sklearn.pipeline import Pipeline
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 1 for calling a global function with example usage: _make_augmentation_pipeline(augmentation_list) and returns: pipeline