code: def test_flaxwav2vec2bert_pt_flax_equivalence(self):
      pt_model = SpeechEncoderDecoderModel.from_pretrained(
          "speech-seq2seq/wav2vec2-2-bert-large"
      )
      fx_model = FlaxSpeechEncoderDecoderModel.from_pretrained(
          "speech-seq2seq/wav2vec2-2-bert-large", from_pt=True
      )
  
      pt_model.to(torch_device)
      pt_model.eval()
  
      # prepare inputs
      batch_size = 13
      input_values = floats_tensor(
          [batch_size, 512], fx_model.config.encoder.vocab_size
      )
      attention_mask = random_attention_mask([batch_size, 512])
      decoder_input_ids = ids_tensor(
          [batch_size, 4], fx_model.config.decoder.vocab_size
      )
      decoder_attention_mask = random_attention_mask([batch_size, 4])
      inputs_dict = {
          "inputs": input_values,
          "attention_mask": attention_mask,
          "decoder_input_ids": decoder_input_ids,
          "decoder_attention_mask": decoder_attention_mask,
      }
  
      flax_inputs = inputs_dict
      pt_inputs = {
          k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()
      }
  
      with torch.no_grad():
          pt_outputs = pt_model(**pt_inputs)
      pt_logits = pt_outputs.logits
      pt_outputs = pt_outputs.to_tuple()
  
      fx_outputs = fx_model(**inputs_dict)
      fx_logits = fx_outputs.logits
      fx_outputs = fx_outputs.to_tuple()
  
      self.assertEqual(
          len(fx_outputs),
          len(pt_outputs),
          "Output lengths differ between Flax and PyTorch",
      )
      self.assert_almost_equals(fx_logits, pt_logits.numpy(), 4e-2)
  
      # PT -> Flax
      with tempfile.TemporaryDirectory() as tmpdirname:
          pt_model.save_pretrained(tmpdirname)
          fx_model_loaded = (
              FlaxSpeechEncoderDecoderModel.from_pretrained(
                  tmpdirname, from_pt=True
              )
          )
  
      fx_outputs_loaded = fx_model_loaded(**inputs_dict)
      fx_logits_loaded = fx_outputs_loaded.logits
      fx_outputs_loaded = fx_outputs_loaded.to_tuple()
      self.assertEqual(
          len(fx_outputs_loaded),
          len(pt_outputs),
          "Output lengths differ between Flax and PyTorch",
      )
      self.assert_almost_equals(
          fx_logits_loaded, pt_logits.numpy(), 4e-2
      )
  
      # Flax -> PT
      with tempfile.TemporaryDirectory() as tmpdirname:
          fx_model.save_pretrained(tmpdirname)
          pt_model_loaded = SpeechEncoderDecoderModel.from_pretrained(
              tmpdirname, from_flax=True
          )
  
      pt_model_loaded.to(torch_device)
      pt_model_loaded.eval()
  
      with torch.no_grad():
          pt_outputs_loaded = pt_model_loaded(**pt_inputs)
      pt_logits_loaded = pt_outputs_loaded.logits
      pt_outputs_loaded = pt_outputs_loaded.to_tuple()
  
      self.assertEqual(
          len(fx_outputs),
          len(pt_outputs_loaded),
          "Output lengths differ between Flax and PyTorch",
      )
      self.assert_almost_equals(
          fx_logits, pt_logits_loaded.numpy(), 4e-2
      )
  
imports: import tempfile
  import unittest
  import numpy
  from transformers import is_flax_available, is_torch_available
  from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow, torch_device
  from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
  from ..bart.test_modeling_flax_bart import FlaxBartStandaloneDecoderModelTester
  from ..bert.test_modeling_flax_bert import FlaxBertModelTester
  from ..gpt2.test_modeling_flax_gpt2 import FlaxGPT2ModelTester
  from ..wav2vec2.test_modeling_flax_wav2vec2 import FlaxWav2Vec2ModelTester
label_desc: Use image node_id 4 for calling the FlaxWav2Vec2BertModelTest obj's underlying member method code with example usage: obj.test_flaxwav2vec2bert_pt_flax_equivalence() without return types