code: def test_Encoder_forward_backward(extracted, freeze_finetune_updates):
      encoder = FairseqAVHubertEncoder(
          20,
          freeze_finetune_updates=freeze_finetune_updates,
          encoder_embed_dim=16,
          encoder_layers=2,
          encoder_ffn_embed_dim=64,
          encoder_attention_heads=2,
          extracted=extracted,
          pretrain=False,
      )
      if extracted:
          x = torch.randn(2, 10, 32, requires_grad=True)
          x_lens = torch.LongTensor([10, 5])
          encoder.train()
          y, _, _ = encoder(x, x_lens)
          if freeze_finetune_updates == 0:
              y.sum().backward()
          else:
              y.sum()  # requires_grad=False if freezing
      else:
          x = {
              "video": torch.randn(2, 1, 10, 88, 88),
              "audio": torch.randn(2, 26 * 4, 10),
          }
          x_lens = torch.LongTensor([10, 5])
          encoder.train()
          y, _, _ = encoder(x, x_lens)
          if freeze_finetune_updates == 0:
              y.sum().backward()
          else:
              y.sum()
  
imports: import pytest
  import torch
  from espnet2.asr.encoder.avhubert_encoder import FairseqAVHubertEncoder
label_desc: Use image node_id 1 for calling a global function with example usage: test_Encoder_forward_backward(extracted, freeze_finetune_updates) without return types