code: def sample(path, start, length):
      """
      :param path: path to the model
      :param start: a `str`. the starting characters
      :param length: a `int`. the length of text to generate
      """
      # initialize vocabulary and sequence length
      param.seq_len = 1
      ds = CharRNNData(param.corpus, 100000)
  
      pred = OfflinePredictor(
          PredictConfig(
              model=Model(),
              session_init=SmartInit(path),
              input_names=["input", "c0", "h0", "c1", "h1"],
              output_names=["prob", "last_state"],
          )
      )
  
      # feed the starting sentence
      initial = np.zeros((1, param.rnn_size))
      for c in start[:-1]:
          x = np.array([[ds.char2idx[c]]], dtype="int32")
          _, state = pred(x, initial, initial, initial, initial)
  
      def pick(prob):
          t = np.cumsum(prob)
          s = np.sum(prob)
          return int(np.searchsorted(t, np.random.rand(1) * s))
  
      # generate more
      ret = start
      c = start[-1]
      for _ in range(length):
          x = np.array([[ds.char2idx[c]]], dtype="int32")
          prob, state = pred(
              x, state[0, 0], state[0, 1], state[1, 0], state[1, 1]
          )
          c = ds.chars[pick(prob[0])]
          ret += c
      print(ret)
  
imports: import argparse
  import numpy
  import operator
  import os
  import sys
  from collections import Counter
  import tensorflow
  from tensorpack import *
  from tensorpack.tfutils import optimizer, summary
  from tensorpack.tfutils.gradproc import GlobalNormClip
label_desc: Use image node_id 2 for calling a global function with example usage: sample(path, start, length) and returns: int