code: def predict_char(prefix, num_predicts, model, vocab):
      model.init_state(batch_size=1)
      outputs = [vocab[prefix[0]]]
      get_input = lambda: one_hot(
          jn.array([outputs[-1]]).reshape(1, 1), len(vocab)
      )
      for y in prefix[1:]:  # Warmup state with prefix
          model(get_input())
          outputs.append(vocab[y])
      for _ in range(num_predicts):  # Predict num_predicts steps
          Y = model(get_input())
          outc = int(Y.argmax(axis=1).reshape(1))
          outputs.append(outc)
      return "".join([vocab.idx_to_token[i] for i in outputs])
  
imports: import collections
  import random
  import re
  import jax
  import jax.numpy
  import tensorflow_datasets
  import objax
  from objax.functional import one_hot
  from objax.zoo.rnn import RNN
label_desc: Use image node_id 6 for calling a global function with example usage: predict_char(prefix, num_predicts, model, vocab) and returns: str