class_docstr: Base class for discrete data. 
class_docstr_tok: ["Base","class","for","discrete","data","."]
docstr: Extracts data from a source and returns it in a user specified format.
  
  It takes in as input the name for a concept folder that lies inside of the
  source_dir.
  
  Args
    concept: name of the concept (e.g., name of a folder that contains concept
    examples in source_dir). They should be located in source_dir.
  Returns:
    data parsed
docstr_tok: ["Extracts","data","from","a","source","and","returns","it","in","a","user","specified","format",".","It","takes","in","as","input","the","name","for","a","concept","folder","that","lies","inside","of","the","source_dir",".","Args","concept",":","name","of","the","concept","(","e.g.",",","name","of","a","folder","that","contains","concept","examples","in","source_dir",")",".","They","should","be","located","in","source_dir",".","Returns",":","data","parsed"]
code: def load_data(self, concept):
      """Extracts data from a source and returns it in a user specified format.
  
      It takes in as input the name for a concept folder that lies inside of the
      source_dir.
  
      Args
        concept: name of the concept (e.g., name of a folder that contains concept
        examples in source_dir). They should be located in source_dir.
      Returns:
        data parsed
      """
      # Needs to be implemented
      raise NotImplementedError()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from abc import ABCMeta
  from abc import abstractmethod
  from multiprocessing import dummy
  import os
  import os.path
  import numpy
  import PIL.Image
  import six
  import tensorflow
label_desc: Use image node_id 3 for calling the DiscreteActivationGeneratorBase obj's underlying member method code with example usage: obj.load_data(concept) without return types