class_docstr: Converts integer node ID's to human readable labels.
docstr: Loads a human readable English name for each softmax node.
  Args:
    label_lookup_path: string UID to integer node ID.
    uid_lookup_path: string UID to human-readable string.
  Returns:
    dict from integer node ID to human-readable string.
docstr_tok: ["Loads","a","human","readable","English","name","for","each","softmax","node",".","Args",":","label_lookup_path",":","string","UID","to","integer","node","ID",".","uid_lookup_path",":","string","UID","to","human-readable","string",".","Returns",":","dict","from","integer","node","ID","to","human-readable","string","."]
code: def load(self, label_lookup_path, uid_lookup_path):
      """Loads a human readable English name for each softmax node.
      Args:
        label_lookup_path: string UID to integer node ID.
        uid_lookup_path: string UID to human-readable string.
      Returns:
        dict from integer node ID to human-readable string.
      """
      if not tf.gfile.Exists(uid_lookup_path):
          tf.logging.fatal("File does not exist %s", uid_lookup_path)
      if not tf.gfile.Exists(label_lookup_path):
          tf.logging.fatal("File does not exist %s", label_lookup_path)
  
      # Loads mapping from string UID to human-readable string
      proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
      uid_to_human = {}
      p = re.compile(r"[n\d]*[ \S,]*")
      for line in proto_as_ascii_lines:
          parsed_items = p.findall(line)
          uid = parsed_items[0]
          human_string = parsed_items[2]
          uid_to_human[uid] = human_string
  
      # Loads mapping from string UID to integer node ID.
      node_id_to_uid = {}
      proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
      for line in proto_as_ascii:
          if line.startswith("  target_class:"):
              target_class = int(line.split(": ")[1])
          if line.startswith("  target_class_string:"):
              target_class_string = line.split(": ")[1]
              node_id_to_uid[target_class] = target_class_string[1:-2]
  
      # Loads the final mapping of integer node ID to human-readable string
      node_id_to_name = {}
      for key, val in list(node_id_to_uid.items()):
          if val not in uid_to_human:
              tf.logging.fatal("Failed to locate: %s", val)
          name = uid_to_human[val]
          node_id_to_name[key] = name
  
      return node_id_to_name
  
file_path: AdvBox/tools.py
imports: from __future__ import division
  from __future__ import print_function
  from builtins import object
  from past.utils import old_div
  import numpy
  import matplotlib.pyplot
  import tensorflow
  import re
label_desc: Use image node_id 2 for calling the NodeLookup obj's underlying member method code with example usage: obj.load(label_lookup_path, uid_lookup_path) and returns: node_id_to_name