class_docstr: Defines a pipeline for transforming sequence data.
  
  The input is assumed to be utf-8 encoded `str` (Python 3) or
  `unicode` (Python 2).
  
  Attributes:
      convert_token: The function to apply to input sequence data.
      pipes: The Pipelines that will be applid to input sequence
          data in order.
docstr: Add a Pipeline to be applied after this processing pipeline.
  
  Arguments:
      pipeline: The Pipeline or callable to apply after this
          Pipeline.
docstr_tok: ["Add","a","Pipeline","to","be","applied","after","this","processing","pipeline",".","Arguments",":","pipeline",":","The","Pipeline","or","callable","to","apply","after","this","Pipeline","."]
code: def add_after(self, pipeline):
      """Add a Pipeline to be applied after this processing pipeline.
  
      Arguments:
          pipeline: The Pipeline or callable to apply after this
              Pipeline.
      """
      if not isinstance(pipeline, Pipeline):
          pipeline = Pipeline(pipeline)
      self.pipes = self.pipes[:] + pipeline.pipes[:]
      return self
  
file_path: decanlp/text/torchtext/data/pipeline.py
label_desc: Use image node_id 5 for calling the Pipeline obj's underlying member method code with example usage: obj.add_after(pipeline) and returns: self