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: Make a CAV instance from a saved CAV (pickle file).
  
  Args:
    cav_path: the location of the saved CAV
  
  Returns:
    CAV instance.
docstr_tok: ["Make","a","CAV","instance","from","a","saved","CAV","(","pickle","file",")",".","Args",":","cav_path",":","the","location","of","the","saved","CAV","Returns",":","CAV","instance","."]
code: def load_cav(cav_path):
      """Make a CAV instance from a saved CAV (pickle file).
  
      Args:
        cav_path: the location of the saved CAV
  
      Returns:
        CAV instance.
      """
      with tf.io.gfile.GFile(cav_path, "rb") as pkl_file:
          save_dict = pickle.load(pkl_file)
  
      cav = CAV(
          save_dict["concepts"],
          save_dict["bottleneck"],
          save_dict["hparams"],
          save_dict["saved_path"],
      )
      cav.accuracies = save_dict["accuracies"]
      cav.cavs = save_dict["cavs"]
      return cav
  
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 2 for calling the CAV obj's underlying member method code with example usage: obj.load_cav(cav_path) and returns: cav