class_docstr: Online beam search implementation.
  
  This simulates streaming decoding.
  It requires encoded features of entire utterance and
  extracts block by block from it as it shoud be done
  in streaming processing.
  This is based on Tsunoo et al, "STREAMING TRANSFORMER ASR
  WITH BLOCKWISE SYNCHRONOUS BEAM SEARCH"
  (https://arxiv.org/abs/2006.14941).
docstr: Recognize one block.
docstr_tok: ["Recognize","one","block","."]
code: def process_one_block(self, h, is_final, maxlen, minlen, maxlenratio):
      """Recognize one block."""
      # extend states for ctc
      self.extend(h, self.running_hyps)
      while self.process_idx < maxlen:
          logging.debug("position " + str(self.process_idx))
          best = self.search(self.running_hyps, h)
  
          if self.process_idx == maxlen - 1:
              # end decoding
              self.running_hyps = self.post_process(
                  self.process_idx,
                  maxlen,
                  minlen,
                  maxlenratio,
                  best,
                  self.ended_hyps,
              )
          n_batch = best.yseq.shape[0]
          local_ended_hyps = []
          is_local_eos = (
              best.yseq[torch.arange(n_batch), best.length - 1]
              == self.eos
          )
          prev_repeat = False
          for i in range(is_local_eos.shape[0]):
              if is_local_eos[i]:
                  hyp = self._select(best, i)
                  local_ended_hyps.append(hyp)
              # NOTE(tsunoo): check repetitions here
              # This is a implicit implementation of
              # Eq (11) in https://arxiv.org/abs/2006.14941
              # A flag prev_repeat is used instead of using set
              # NOTE(fujihara): I made it possible to turned off
              # the below lines using disable_repetition_detection flag,
              # because this criteria is too sensitive that the beam
              # search starts only after the entire inputs are available.
              # Empirically, this flag didn't affect the performance.
              elif (
                  not self.disable_repetition_detection
                  and not prev_repeat
                  and best.yseq[i, -1] in best.yseq[i, :-1]
                  and not is_final
              ):
                  prev_repeat = True
          if prev_repeat:
              logging.info("Detected repetition.")
              break
  
          if (
              is_final
              and maxlenratio == 0.0
              and end_detect(
                  [lh.asdict() for lh in self.ended_hyps],
                  self.process_idx,
              )
          ):
              logging.info(f"end detected at {self.process_idx}")
              return self.assemble_hyps(self.ended_hyps)
  
          if len(local_ended_hyps) > 0 and not is_final:
              logging.info(
                  "Detected hyp(s) reaching EOS in this block."
              )
              break
              # breaking here means that prev hyps
              # is 2 behind the ended hyps, not 1
  
          self.prev_hyps = self.running_hyps
          self.running_hyps = self.post_process(
              self.process_idx,
              maxlen,
              minlen,
              maxlenratio,
              best,
              self.ended_hyps,
          )
  
          if is_final:
              for hyp in local_ended_hyps:
                  self.ended_hyps.append(hyp)
  
          if len(self.running_hyps) == 0:
              logging.info("no hypothesis. Finish decoding.")
              return self.assemble_hyps(self.ended_hyps)
          else:
              logging.debug(
                  f"remained hypotheses: {len(self.running_hyps)}"
              )
          # increment number
          self.process_idx += 1
  
      if is_final:
          return self.assemble_hyps(self.ended_hyps)
      else:
          try:
              local_ended_hyps
          except Exception:
              local_ended_hyps = []
  
          for hyp in self.ended_hyps:
              local_ended_hyps.append(hyp)
          rets = self.assemble_hyps(local_ended_hyps)
  
          if self.process_idx > 1 and len(self.prev_hyps) > 0:
              self.running_hyps = self.prev_hyps
              self.process_idx -= 1
              self.prev_hyps = []
  
          # N-best results
          return rets
  
imports: import logging
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Tuple
  import torch
  from espnet2.asr.transducer.beam_search_transducer_streaming import BeamSearchTransducerStreaming
  from espnet.nets.batch_beam_search import BatchBeamSearch
  from espnet.nets.batch_beam_search import BatchHypothesis
  from espnet.nets.beam_search import Hypothesis
  from espnet.nets.beam_search_timesync_streaming import BeamSearchTimeSyncStreaming
  from espnet.nets.e2e_asr_common import end_detect
label_desc: Use image node_id 6 for calling the BatchBeamSearchOnline obj's underlying member method code with example usage: obj.process_one_block(h, is_final, maxlen, minlen, maxlenratio) and returns: self, rets, self, self