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 serialized file
  
  Args:
  filename: location of serialized Pipeline object
  
  >>> import tempfile
  >>> fp = tempfile.NamedTemporaryFile()
  >>> Pipeline(['NSentences', ('CleanText', {'some': 'arg'})]).save(fp.name)
  >>> p = Pipeline.load(fp.name)
  >>> fp.close()
  >>> public_flds = dict(filter(lambda i: not i[0].startswith('_'), p.__dict__.items()))
  >>> sorted(public_flds.items())
  [('hint_language', None), ('kwargs', {}), ('language', None), ('steps', [('NSentences', {}), ('CleanText', {'some': 'arg'})])]
docstr_tok: ["#","pylint",":","disable=line-too-long","Loads","pipeline","from","serialized","file","Args",":","filename",":","location","of","serialized","Pipeline","object",">",">",">","import","tempfile",">",">",">","fp","=","tempfile.NamedTemporaryFile","(",")",">",">",">","Pipeline","(","[","'NSentences","'",",","(","'CleanText","'",",","{","'some","'",":","'arg","'","}",")","]",")",".save","(","fp.name",")",">",">",">","p","=","Pipeline.load","(","fp.name",")",">",">",">","fp.close","(",")",">",">",">","public_flds","=","dict","(","filter","(","lambda","i",":","not","i","[","0","]",".startswith","(","'","_","'",")",",","p.__dict__.items","(",")",")",")",">",">",">","sorted","(","public_flds.items","(",")",")","[","(","'hint_language","'",",","None",")",",","(","'kwargs","'",",","{","}",")",",","(","'language","'",",","None",")",",","(","'steps","'",",","[","(","'NSentences","'",",","{","}",")",",","(","'CleanText","'",",","{","'some","'",":","'arg","'","}",")","]",")","]"]
code: def load(filename):
      """# pylint: disable=line-too-long
      Loads pipeline from serialized file
  
      Args:
      filename: location of serialized Pipeline object
  
      >>> import tempfile
      >>> fp = tempfile.NamedTemporaryFile()
      >>> Pipeline(['NSentences', ('CleanText', {'some': 'arg'})]).save(fp.name)
      >>> p = Pipeline.load(fp.name)
      >>> fp.close()
      >>> public_flds = dict(filter(lambda i: not i[0].startswith('_'), p.__dict__.items()))
      >>> sorted(public_flds.items())
      [('hint_language', None), ('kwargs', {}), ('language', None), ('steps', [('NSentences', {}), ('CleanText', {'some': 'arg'})])]
      """
      with open(filename, "r") as json_file:
          dict_representation = json.load(json_file)
          return Pipeline.from_dict(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 5 for calling the Pipeline obj's underlying member method code with example usage: obj.load(filename) and returns: Pipeline