class_docstr: infer from forzen model 
docstr: infer prediction results 
code: def predict(self):
      """infer prediction results"""
      batch = 0
  
      solver_name = self.config["solver"]["name"]
      solver = registers.solver[solver_name](self.config)
  
      with self.graph.as_default():
          dataset, _ = solver.input_data(self._mode)
          iterator = dataset.make_one_shot_iterator()
          next_element = iterator.get_next()
  
      target_seq_list, predict_seq_list = [], []
      num_input_samples, num_processed_samples = 0, 0
      try:
          while True:
              batch += 1
              logging.info("batch : {}".format(batch))
  
              features, _ = self.sess.run(next_element)
              inputs = features["inputs"]
              input_length = features["input_length"]
              y_true_valid = features["targets"]
              num_input_samples += input_length.shape[0]
              logging.info(
                  "The size of the INFER Set increased to {}".format(
                      num_input_samples
                  )
              )
  
              validate_feed = {
                  self.inputs: inputs,
                  self.input_length: input_length,
              }
              y_pred_valid = self.sess.run(
                  self.pred_valid, feed_dict=validate_feed
              )
              num_processed_samples += y_pred_valid.shape[0]
              logging.info(
                  "A total of {} samples has been successfully processed".format(
                      num_processed_samples
                  )
              )
  
              target_seq_list.extend(y_true_valid.tolist())
              predict_seq_list.extend(y_pred_valid.tolist())
  
      except tf.errors.OutOfRangeError:
          logging.info("Infer End")
  
      token_errors = metrics_lib.token_error(
          predict_seq_list=predict_seq_list,
          target_seq_list=target_seq_list,
          eos_id=0,
      )
      logging.info("Token ERR: {}".format(token_errors))
  
file_path: delta/delta/serving/eval_asr_pb.py
imports: import delta.compat
  from absl import logging
  from delta import utils
  from delta.utils import metrics
  from delta.utils.register import registers
  from delta.utils.register import import_all_modules_for_register
  from delta.serving.base_frozen_model import Evaluater
label_desc: Use image node_id 3 for calling the ASREvaluater obj's underlying member method code with example usage: obj.predict() without return types