class_docstr: Simple model to use for testing. We use an encoder-decoder architecture with tied
  embeddings to make sure we cover enough edge cases.
class_docstr_tok: ["Simple","model","to","use","for","testing",".","We","use","an","encoder-decoder","architecture","with","tied","embeddings","to","make","sure","we","cover","enough","edge","cases","."]
code: def forward(self, x):
      x = self.embedding(x)
      x = self.emb_proj(x)
      x = self.encoder(x)
      x = self.decoder(x)
      return x
  
imports: import os
  from os import PathLike
  from typing import Union, Dict, Optional
  import torch
  from torch.cuda import amp
  from torch.testing import assert_allclose
  import pytest
  from allennlp.common.testing import AllenNlpTestCase, run_distributed_test, requires_multi_gpu
  from allennlp.nn.util import load_state_dict_distributed
  from allennlp.nn.parallel import FairScaleFsdpAccelerator, FairScaleFsdpWrappedModel, ShardedModuleMixin
label_desc: Use image node_id 3 for calling the EncoderDecoderModel obj's underlying member method code with example usage: obj.forward(x) and returns: x