class_docstr: ModelWrapper for the KDD99 dataset from sklearn.
  
  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
  
  Trains a model and saves it to model_path if None exists
  To see it in action, please check kdd99_discrete_example.ipynb
class_docstr_tok: ["ModelWrapper","for","the","KDD99","dataset","from","sklearn",".","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","Trains","a","model","and","saves","it","to","model_path","if","None","exists","To","see","it","in","action",",","please","check","kdd99_discrete_example.ipynb"]
code: def __init__(self, sess, model_path, labels_path):
      if not tf.io.gfile.exists(labels_path):
          raise Exception(
              "Labels path does not exist. Please provide a labels file."
          )
      # Train a model if we have none
      if not tf.io.gfile.exists(model_path):
          train_and_save_model(model_path, labels_path)
  
      # Initialize the wrapper
      super(KDD99KerasModelWrapper, self).__init__(
          sess, model_path, labels_path
      )
  
      # Using SparseCategoricalCrossEntropy here
      self.y_input = tf.compat.v1.placeholder(tf.int64, shape=[None])
      self.loss = self.model.loss_functions[0](
          self.y_input, self.model.outputs[0]
      )
      self._make_gradient_tensors()
  
imports: from tcav.tcav_examples.discrete.kdd99_model import train_and_save_model
  from tcav.model import KerasModelWrapper
  import tensorflow
label_desc: Use image node_id 1 to create a new KDD99KerasModelWrapper object from inherited base classes: KerasModelWrapper with example: obj = KDD99KerasModelWrapper(sess, model_path, labels_path)