code: def test_embedding_expanded_decoder(load_origin_token_embedding):
      expanded_dim = 200
      vocab_size = VOCAB_SIZE_WHISPER_MULTILINGUAL + expanded_dim
      decoder = OpenAIWhisperDecoder(
          vocab_size=vocab_size,
          encoder_output_size=384,
          dropout_rate=0,
          whisper_model="tiny",
          load_origin_token_embedding=load_origin_token_embedding,
      )
      hs_pad = torch.randn(
          4, 100, 384, device=next(decoder.parameters()).device
      )
      ys_in_pad = torch.randint(
          0, 3000, (4, 10), device=next(decoder.parameters()).device
      )
      out, _ = decoder(hs_pad, None, ys_in_pad, None)
      assert out.size() == torch.Size([4, 10, vocab_size])
      out.sum().backward()
  
file_path: espnet/test/espnet2/asr/decoder/test_whisper_decoder.py
imports: import sys
  import pytest
  import torch
  from packaging.version import parse
  from espnet2.asr.decoder.whisper_decoder import OpenAIWhisperDecoder
label_desc: Use image node_id 3 for calling a global function with example usage: test_embedding_expanded_decoder(load_origin_token_embedding) without return types