class_docstr: Activation generator for the KDD99 dataset.
  
  This uses the KDD99 dataset from sklearn. It is automatically loaded when we
  try to train models, so no data downloading is required.
  
  You can read more about it here:
  https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_kddcup99.html
  
  To see it in action, please check kdd99_discrete_example.ipynb
class_docstr_tok: ["Activation","generator","for","the","KDD99","dataset",".","This","uses","the","KDD99","dataset","from","sklearn",".","It","is","automatically","loaded","when","we","try","to","train","models",",","so","no","data","downloading","is","required",".","You","can","read","more","about","it","here",":","https",":","\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.datasets.fetch_kddcup99.html","To","see","it","in","action",",","please","check","kdd99_discrete_example.ipynb"]
code: def __init__(self, model, source_dir, acts_dir, max_examples):
      self.source_dir = source_dir
      super(KDD99DiscreteActivationGenerator, self).__init__(
          model, source_dir, acts_dir, max_examples
      )
  
file_path: tcav/tcav/tcav_examples/discrete/kdd99_activation_generator.py
imports: import os
  from tcav.activation_generator import DiscreteActivationGeneratorBase
  from tcav.tcav_examples.discrete.kdd99_model import encode_variables, kBytesIndices, kFloatIndices, kIntIndices
  import numpy
  import tensorflow
label_desc: Use image node_id 1 to create a new KDD99DiscreteActivationGenerator object from inherited base classes: DiscreteActivationGeneratorBase with example: obj = KDD99DiscreteActivationGenerator(model, source_dir, acts_dir, max_examples)