class_docstr: Converts integer node ID's to human readable labels.
class_docstr_tok: ["Converts","integer","node","ID","'s","to","human","readable","labels","."]
docstr: Loads a human readable English name for each softmax node.
  
  Parameters
  ----------
  label_lookup_path: String
      File containing String UID to integer node ID mapping .
  
  uid_lookup_path: String
      File containing String UID to human-readable string mapping.
  
  Returns
  -------
  node_id_to_name: dict
      dict from integer node ID to human-readable string.
docstr_tok: ["Loads","a","human","readable","English","name","for","each","softmax","node",".","Parameters","--","--","--","--","--","label_lookup_path",":","String","File","containing","String","UID","to","integer","node","ID","mapping",".","uid_lookup_path",":","String","File","containing","String","UID","to","human-readable","string","mapping",".","Returns","--","--","--","-","node_id_to_name",":","dict","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.
  
      Parameters
      ----------
      label_lookup_path: String
          File containing String UID to integer node ID mapping .
  
      uid_lookup_path: String
          File containing String UID to human-readable string mapping.
  
      Returns
      -------
      node_id_to_name: dict
          dict from integer node ID to human-readable string.
  
      """
      if not tf_compat_v1.gfile.Exists(uid_lookup_path):
          tf.logging.fatal("File does not exist %s", uid_lookup_path)
      if not tf_compat_v1.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_compat_v1.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_compat_v1.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 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
  
imports: import re
  import os.path
  import collections
  import numpy
  import tensorflow
  from tensorflow.core.framework import graph_pb2
  import tvm
  from tvm.contrib.download import download_testdata
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