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: Apply the the current Pipeline(s) to an input.
  
  Arguments:
      x: The input to process with the Pipeline(s).
      Positional arguments: Forwarded to the `call` function
          of the Pipeline(s).
docstr_tok: ["Apply","the","the","current","Pipeline","(","s",")","to","an","input",".","Arguments",":","x",":","The","input","to","process","with","the","Pipeline","(","s",")",".","Positional","arguments",":","Forwarded","to","the","`","call","`","function","of","the","Pipeline","(","s",")","."]
code: def __call__(self, x, *args):
      """Apply the the current Pipeline(s) to an input.
  
      Arguments:
          x: The input to process with the Pipeline(s).
          Positional arguments: Forwarded to the `call` function
              of the Pipeline(s).
      """
      for pipe in self.pipes:
          x = pipe.call(x, *args)
      return x
  
file_path: decanlp/text/torchtext/data/pipeline.py
label_desc: Use image node_id 2 for calling the Pipeline obj's underlying member method code with example usage: obj.__call__(x) and returns: x