code: def __init__(
      self,
      out_vocab_size,
      embed_tokens,
      encoder_hidden_dim,
      embed_dim,
      hidden_dim,
      out_embed_dim,
      cell_type,
      num_layers,
      dropout_in,
      dropout_out,
      attention_type,
      attention_heads,
      first_layer_attention,
      averaging_encoder,
  ):
      DecoderWithLinearOutputProjection.__init__(
          self, out_vocab_size, out_embed_dim=out_embed_dim
      )
      RNNDecoderBase.__init__(
          self,
          embed_tokens,
          encoder_hidden_dim,
          embed_dim,
          hidden_dim,
          out_embed_dim,
          cell_type,
          num_layers,
          dropout_in,
          dropout_out,
          attention_type,
          attention_heads,
          first_layer_attention,
          averaging_encoder,
      )
      log_class_usage(__class__)
  
imports: from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from fairseq import utils
  from pytext.config import ConfigBase
  from pytext.models.seq_models.base import PyTextSeq2SeqModule
  from pytext.utils.usage import log_class_usage
  from torch import nn
  from .attention import DotAttention
  from .base import PlaceholderIdentity, PyTextIncrementalDecoderComponent
label_desc: Use image node_id 1 to create a new RNNDecoder object from inherited base classes: RNNDecoderBase, DecoderWithLinearOutputProjection with example: obj = RNNDecoder(out_vocab_size, embed_tokens, encoder_hidden_dim, embed_dim, hidden_dim, out_embed_dim, cell_type, num_layers, dropout_in, dropout_out, attention_type, attention_heads, first_layer_attention, averaging_encoder)