code: def _update_state(
      self,
      state: ConstraintStateType,
      last_prediction: torch.Tensor,
  ) -> ConstraintStateType:
      for i, batch in enumerate(state):
          for j, beam in enumerate(batch):
              prediction = last_prediction[i, j].item()
              prefix = beam["current_prefix"]
              seen_ngrams = beam["seen_ngrams"]
  
              if len(prefix) == self.ngram_size - 1:
                  # This is a new ngram that we have to remember
                  if tuple(prefix) not in seen_ngrams:
                      seen_ngrams[tuple(prefix)] = []
                  seen_ngrams[tuple(prefix)].append(prediction)
  
              # Create the new prefix, removing the oldest index if the prefix
              # is too long
              prefix.append(prediction)
              if len(prefix) == self.ngram_size:
                  prefix.pop(0)
      return state
  
imports: from inspect import signature
  from typing import Any, List, Callable, Tuple, Dict, cast, TypeVar, Optional
  import copy
  import warnings
  import torch
  from allennlp.common import Lazy, Registrable
  from allennlp.common.checks import ConfigurationError
  from allennlp.data import Vocabulary
  from allennlp.nn.util import min_value_of_dtype
label_desc: Use image node_id 4 for calling the RepeatedNGramBlockingConstraint obj's underlying member method code with example usage: obj._update_state(state, last_prediction) and returns: state