class_docstr: A GRU copying decoder that accepts an variable-sized set of encodings.
class_docstr_tok: ["A","GRU","copying","decoder","that","accepts","an","variable-sized","set","of","encodings","."]
code: def finalize_metadata(self) -> None:
      self.__token_counter[self.START] = 1000000
      self.__token_counter[self.END] = 1000000
      self.__output_vocabulary = Vocabulary.create_vocabulary(
          self.__token_counter,
          max_size=self.vocabulary_max_size,
          count_threshold=self.vocabulary_count_threshold,
      )
      self.LOGGER.info(
          "Output vocabulary Size %s", len(self.__output_vocabulary)
      )
      del self.__token_counter
  
imports: from typing_extensions import Final
  import logging
  import math
  import numpy
  import torch
  import torch.nn
  from dpu_utils.mlutils import Vocabulary
  from torch_scatter import scatter_add
  from torch_scatter.composite import scatter_log_softmax, scatter_logsumexp
  from typing import Any, Counter, Dict, List, NamedTuple, Tuple, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel
label_desc: Use image node_id 6 for calling the GruCopyingDecoderModel obj's underlying member method code with example usage: obj.finalize_metadata() without return types