class_docstr: Apply sequential transformations to input sample.
  Default generate transformed samples of combination number of contained
  transformations.
code: def __init__(self, transform_objs):
      Transformation.__init__(self)
      list.__init__(self, [])
  
      if not isinstance(transform_objs, list):
          transform_objs = [transform_objs]
  
      # add unique legal transformations
      for transform_obj in transform_objs:
          self.append(transform_obj)
  
file_path: textflint/textflint/generation/transformation/pipeline.py
imports: from ..transformation import Transformation
label_desc: Use image node_id 1 to create a new Pipeline object from inherited base classes: Transformation, list with example: obj = Pipeline(transform_objs)