class_docstr: Convolutional Neural Network used for image classification, whose
  architecture can be any of the `VALID_ARCHITECTURES`.
  
  This class wraps the `tf.slim` implementations of these models, with some
  helpful additions.
class_docstr_tok: ["Convolutional","Neural","Network","used","for","image","classification",",","whose","architecture","can","be","any","of","the","`","VALID_ARCHITECTURES","`",".","This","class","wraps","the","`","tf.slim","`","implementations","of","these","models",",","with","some","helpful","additions","."]
docstr: Returns the vars which the base network checkpoint will load into.
  
  We return a dict which maps a variable name to a variable object. This
  is needed because the base network may be created inside a particular
  scope, which the checkpoint may not contain. Therefore we must map
  each variable to its unscoped name in order to be able to find them in
  the checkpoint file.
docstr_tok: ["Returns","the","vars","which","the","base","network","checkpoint","will","load","into",".","We","return","a","dict","which","maps","a","variable","name","to","a","variable","object",".","This","is","needed","because","the","base","network","may","be","created","inside","a","particular","scope",",","which","the","checkpoint","may","not","contain",".","Therefore","we","must","map","each","variable","to","its","unscoped","name","in","order","to","be","able","to","find","them","in","the","checkpoint","file","."]
code: def get_base_network_checkpoint_vars(self):
      """Returns the vars which the base network checkpoint will load into.
  
      We return a dict which maps a variable name to a variable object. This
      is needed because the base network may be created inside a particular
      scope, which the checkpoint may not contain. Therefore we must map
      each variable to its unscoped name in order to be able to find them in
      the checkpoint file.
      """
      variable_scope_len = len(self.variable_scope.name) + 1
      var_list = self._get_base_network_vars()
      var_map = {}
      for var in var_list:
          var_name = var.op.name
          checkpoint_var_name = var_name[variable_scope_len:]
          var_map[checkpoint_var_name] = var
      return var_map
  
imports: import functools
  import sonnet
  import tensorflow
  import tensorflow.contrib.slim
  from tensorflow.contrib.slim.nets import resnet_v2, resnet_v1, vgg
  from luminoth.models.base import truncated_vgg
  from luminoth.utils.checkpoint_downloader import get_checkpoint_file
label_desc: Use image node_id 19 for calling the BaseNetwork obj's underlying member method code with example usage: obj.get_base_network_checkpoint_vars() and returns: var_map