class_docstr: Create a pipeline instance based on the elements you would want from your text
  
  >>> pipe = Pipeline(['Raw', ('NWords',), ('CleanText',)])
  >>> sorted(pipe('Test sentence <a=>').items())
  [('CleanText', 'Test sentence'), ('NWords', 2), ('Raw', 'Test sentence <a=>')]
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docstr: Extends the available operations with the given name and callable target function
  :param target: the Keyword that the function will be registered with
  :param args: A Callable target function which should expect a Spacy Doc and kwargs such
  as settings
  :return:
docstr_tok: ["Extends","the","available","operations","with","the","given","name","and","callable","target","function",":","param","target",":","the","Keyword","that","the","function","will","be","registered","with",":","param","args",":","A","Callable","target","function","which","should","expect","a","Spacy","Doc","and","kwargs","such","as","settings",":","return",":"]
code: def register_operation(self, op_name, target_fn):
      """
      Extends the available operations with the given name and callable target function
      :param target: the Keyword that the function will be registered with
      :param args: A Callable target function which should expect a Spacy Doc and kwargs such
      as settings
      :return:
      """
      self._operations[op_name] = target_fn
  
file_path: textpipe/textpipe/pipeline.py
imports: import json
  import spacy
  from textpipe.doc import Doc
  import textpipe.operation
label_desc: Use image node_id 3 for calling the Pipeline obj's underlying member method code with example usage: obj.register_operation(op_name, target_fn) without return types