class_docstr: An implementation of `<https://arxiv.org/abs/1804.07998>`_ adapted from
  `<https://github.com/nesl/nlp_adversarial_examples>`_.
code: def get_words_probs_uncached(self, prefix_words, list_words):
      targets = np.zeros(
          [self.BATCH_SIZE, self.NUM_TIMESTEPS], np.int32
      )
      weights = np.ones(
          [self.BATCH_SIZE, self.NUM_TIMESTEPS], np.float32
      )
  
      if prefix_words.find("<S>") != 0:
          prefix_words = "<S> " + prefix_words
      prefix = [self.vocab.word_to_id(w) for w in prefix_words.split()]
      prefix_char_ids = [
          self.vocab.word_to_char_ids(w) for w in prefix_words.split()
      ]
  
      inputs = np.zeros([self.BATCH_SIZE, self.NUM_TIMESTEPS], np.int32)
      char_ids_inputs = np.zeros(
          [
              self.BATCH_SIZE,
              self.NUM_TIMESTEPS,
              self.vocab.max_word_length,
          ],
          np.int32,
      )
  
      samples = prefix[:]
      char_ids_samples = prefix_char_ids[:]
      inputs = [[samples[-1]]]
      char_ids_inputs[0, 0, :] = char_ids_samples[-1]
      softmax = self.sess.run(
          self.t["softmax_out"],
          feed_dict={
              self.t["char_inputs_in"]: char_ids_inputs,
              self.t["inputs_in"]: inputs,
              self.t["targets_in"]: targets,
              self.t["target_weights_in"]: weights,
          },
      )
      words_ids = [self.vocab.word_to_id(w) for w in list_words]
      word_probs = [softmax[0][w_id] for w_id in words_ids]
      return np.array(word_probs)
  
file_path: TextAttack/textattack/constraints/grammaticality/language_models/google_language_model/alzantot_goog_lm.py
imports: import os
  import lru
  import numpy
  from textattack.shared import utils
  from .None import lm_data_utils, lm_utils
label_desc: Use image node_id 3 for calling the GoogLMHelper obj's underlying member method code with example usage: obj.get_words_probs_uncached(prefix_words, list_words) and returns: np