code: def test_Encoder_forward_backward(
      finetuning, eval, freeze_encoder_updates
  ):
      if not is_torch_1_12_1_plus:
          return
  
      encoder = TorchAudioHuBERTPretrainEncoder(
          20,
          extractor_conv_layer_config=[[3, 3, 2]],
          encoder_pos_conv_kernel=16,
          encoder_pos_conv_groups=4,
          encoder_embed_dim=4,
          encoder_num_layers=1,
          encoder_num_heads=1,
          encoder_ff_interm_features=4,
          num_classes=10,
          final_dim=10,
          finetuning=finetuning,
          freeze_encoder_updates=freeze_encoder_updates,
      )
      x = torch.randn(2, 32, requires_grad=True)
      y = torch.randint(low=0, high=10, size=(2, 15), dtype=torch.long)
      x_lens = torch.LongTensor([32, 16])
      y, _, p = encoder(x, x_lens, y)
      if not eval:
          encoder.train()
          if not finetuning:
              p.sum().backward()
          else:
              if freeze_encoder_updates == 0:
                  y.sum().backward()
              else:
                  y.sum()  # requires_grad=False if freezing
      else:
          encoder.eval()
          y, _, p = encoder(x, x_lens, y)
          y.sum()
  
imports: import pytest
  import torch
  from packaging.version import parse
  from espnet2.asr.encoder.hubert_encoder import TorchAudioHuBERTPretrainEncoder
label_desc: Use image node_id 1 for calling a global function with example usage: test_Encoder_forward_backward(finetuning, eval, freeze_encoder_updates) without return types