class_docstr: CAV class contains methods for concept activation vector (CAV).
  
  CAV represents semenatically meaningful vector directions in
  network's embeddings (bottlenecks).
class_docstr_tok: ["CAV","class","contains","methods","for","concept","activation","vector","(","CAV",")",".","CAV","represents","semenatically","meaningful","vector","directions","in","network","'s","embeddings","(","bottlenecks",")","."]
docstr: HParams used to train the CAV.
  
  you can use logistic regression or linear regression, or different
  regularization of the CAV parameters.
  
  Returns:
    TF.HParams for training.
docstr_tok: ["HParams","used","to","train","the","CAV",".","you","can","use","logistic","regression","or","linear","regression",",","or","different","regularization","of","the","CAV","parameters",".","Returns",":","TF.HParams","for","training","."]
code: def default_hparams():
      """HParams used to train the CAV.
  
      you can use logistic regression or linear regression, or different
      regularization of the CAV parameters.
  
      Returns:
        TF.HParams for training.
      """
      return {
          "model_type": "linear",
          "alpha": 0.01,
          "max_iter": 1000,
          "tol": 1e-3,
      }
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os.path
  import pickle
  import numpy
  from six.moves import range
  from sklearn import linear_model
  from sklearn import metrics
  from sklearn.model_selection import train_test_split
  from tcav import utils
  import tensorflow
label_desc: Use image node_id 1 for calling the CAV obj's underlying member method code with example usage: obj.default_hparams() and returns: dict