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
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docstr: When read from .csv, all variables are parsed as string.
  
  This function assigns the proper types
  
  Args:
    texts: numpy.ndarray. Contains data parsed from dataset.
  
  Returns:
    texts: numpy.ndarray. Returns data with the proper types assigned
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code: def _convert_types(self, texts):
      """When read from .csv, all variables are parsed as string.
  
      This function assigns the proper types
  
      Args:
        texts: numpy.ndarray. Contains data parsed from dataset.
  
      Returns:
        texts: numpy.ndarray. Returns data with the proper types assigned
  
      """
      texts[:, kBytesIndices] = texts[:, kBytesIndices].astype(str)
      texts[:, kFloatIndices] = texts[:, kFloatIndices].astype(
          np.float32
      )
      texts[:, kIntIndices] = texts[:, kIntIndices].astype(np.int)
      return texts
  
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 4 for calling the KDD99DiscreteActivationGenerator obj's underlying member method code with example usage: obj._convert_types(texts) and returns: texts