class_docstr: Represents an encoder-decoder model.
class_docstr_tok: ["Represents","an","encoder-decoder","model","."]
docstr: :param model_dim:
      The dimensionality of the model.
  :param target_vocab_info:
      The vocabulary information of sequences produced by the model.
docstr_tok: [":","param","model_dim",":","The","dimensionality","of","the","model",".",":","param","target_vocab_info",":","The","vocabulary","information","of","sequences","produced","by","the","model","."]
code: def __init__(
      self, model_dim: int, target_vocab_info: VocabularyInfo
  ) -> None:
      """
      :param model_dim:
          The dimensionality of the model.
      :param target_vocab_info:
          The vocabulary information of sequences produced by the model.
      """
      super().__init__(target_vocab_info)
  
      self.model_dim = model_dim
  
file_path: fairseq2/src/fairseq2/models/encoder_decoder.py
imports: from abc import abstractmethod
  from typing import Optional, Tuple
  from torch import Tensor
  from fairseq2.data import VocabularyInfo
  from fairseq2.models.seq2seq import Seq2SeqBatch, Seq2SeqModel
  from fairseq2.models.sequence import SequenceModelOutput
  from fairseq2.nn.incremental_state import IncrementalStateBag
  from fairseq2.nn.padding import PaddingMask
  from fairseq2.typing import override
label_desc: Use image node_id 1 to create a new EncoderDecoderModel object from inherited base classes: Seq2SeqModel with example: obj = EncoderDecoderModel(model_dim, target_vocab_info)