code: def __init__(
      self,
      vocabulary_size: int,
      embedding_size: int,
      hidden_size: int,
      memories_hidden_dim: int,
      unk_id: int,
      dropout_rate: float,
  ):
      super().__init__()
      self.__embedding_layer = nn.Embedding(
          num_embeddings=vocabulary_size, embedding_dim=embedding_size
      )
      self.__output_gru = nn.GRU(
          input_size=embedding_size,
          hidden_size=hidden_size,
          num_layers=1,
          batch_first=True,
      )
  
      self.__unk_id = unk_id
  
      self.__memories_to_standard_attention = nn.Linear(
          in_features=memories_hidden_dim,
          out_features=hidden_size,
          bias=False,
      )
      self.__memories_to_copy_attention = nn.Linear(
          in_features=memories_hidden_dim,
          out_features=hidden_size,
          bias=False,
      )
      self.__hidden_to_vocab = nn.Parameter(
          0.01 * torch.randn((2 * hidden_size, embedding_size))
      )
      self.__vocab_bias = nn.Parameter(torch.zeros(vocabulary_size))
      self.__dropout = nn.Dropout(dropout_rate)
  
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 1 to create a new GruCopyingDecoder object from inherited base classes: nn with example: obj = GruCopyingDecoder(vocabulary_size, embedding_size, hidden_size, memories_hidden_dim, unk_id, dropout_rate)