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=>')]
class_docstr_tok: ["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=",">","'",")","]"]
docstr: # pylint: disable=line-too-long
  Loads pipeline from dictionary
  
  Args:
  dict_representation: A dictionary used to instantiate a pipeline object
  >>> d = {'steps': ['NSentences', ('CleanText', {'some': 'arg'})], 'language': 'it', 'hint_language': None, 'other': 'args'}
  >>> p = Pipeline.from_dict(d)
  >>> public_flds = dict(filter(lambda i: not i[0].startswith('_'), p.__dict__.items()))
  >>> sorted(public_flds.items())
  [('hint_language', None), ('kwargs', {'other': 'args'}), ('language', 'it'), ('steps', [('NSentences', {}), ('CleanText', {'some': 'arg'})])]
docstr_tok: ["#","pylint",":","disable=line-too-long","Loads","pipeline","from","dictionary","Args",":","dict_representation",":","A","dictionary","used","to","instantiate","a","pipeline","object",">",">",">","d","=","{","'steps","'",":","[","'NSentences","'",",","(","'CleanText","'",",","{","'some","'",":","'arg","'","}",")","]",",","'language","'",":","'it","'",",","'hint_language","'",":","None",",","'other","'",":","'args","'","}",">",">",">","p","=","Pipeline.from_dict","(","d",")",">",">",">","public_flds","=","dict","(","filter","(","lambda","i",":","not","i","[","0","]",".startswith","(","'","_","'",")",",","p.__dict__.items","(",")",")",")",">",">",">","sorted","(","public_flds.items","(",")",")","[","(","'hint_language","'",",","None",")",",","(","'kwargs","'",",","{","'other","'",":","'args","'","}",")",",","(","'language","'",",","'it","'",")",",","(","'steps","'",",","[","(","'NSentences","'",",","{","}",")",",","(","'CleanText","'",",","{","'some","'",":","'arg","'","}",")","]",")","]"]
code: def from_dict(dict_representation):
      """# pylint: disable=line-too-long
      Loads pipeline from dictionary
  
      Args:
      dict_representation: A dictionary used to instantiate a pipeline object
      >>> d = {'steps': ['NSentences', ('CleanText', {'some': 'arg'})], 'language': 'it', 'hint_language': None, 'other': 'args'}
      >>> p = Pipeline.from_dict(d)
      >>> public_flds = dict(filter(lambda i: not i[0].startswith('_'), p.__dict__.items()))
      >>> sorted(public_flds.items())
      [('hint_language', None), ('kwargs', {'other': 'args'}), ('language', 'it'), ('steps', [('NSentences', {}), ('CleanText', {'some': 'arg'})])]
      """
      kwargs = dict_representation.pop("kwargs", None)
      if kwargs:
          dict_representation.update(**kwargs)
      return Pipeline(**dict_representation)
  
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 6 for calling the Pipeline obj's underlying member method code with example usage: obj.from_dict(dict_representation) and returns: Pipeline