code: def check_freeze_feature_encoder(
      self,
      config,
      inputs,
      attention_mask,
      encoder_hidden_states,
      decoder_config,
      decoder_input_ids,
      decoder_attention_mask,
      **kwargs,
  ):
      encoder_decoder_config = (
          SpeechEncoderDecoderConfig.from_encoder_decoder_configs(
              config, decoder_config
          )
      )
      enc_dec_model = FlaxSpeechEncoderDecoderModel(
          encoder_decoder_config
      )
      params = enc_dec_model.params
  
      def cross_entropy(logits, labels):
          return -jnp.sum(
              labels * jax.nn.log_softmax(logits, axis=-1), axis=-1
          )
  
      # define a dummy loss function for computing the loss over a forward pass
      def compute_loss(
          params,
          inputs,
          attention_mask,
          decoder_input_ids,
          freeze_feature_encoder: bool = False,
      ):
          outputs_enc_dec = enc_dec_model(
              inputs=inputs,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              freeze_feature_encoder=freeze_feature_encoder,
              params=params,
          )
          logits = outputs_enc_dec.logits
          vocab_size = logits.shape[-1]
          loss = cross_entropy(
              logits,
              onehot(labels=decoder_input_ids, num_classes=vocab_size),
          ).sum()
          return (loss, logits)
  
      # transform the loss function to get the gradients
      grad_fn = jax.value_and_grad(compute_loss, has_aux=True)
  
      # compute the loss, logits, and gradients for the unfrozen model
      (loss, logits), grads = grad_fn(
          params,
          inputs,
          attention_mask,
          decoder_input_ids,
          freeze_feature_encoder=False,
      )
  
      # compare to the loss, logits and gradients for the frozen model
      (loss_frozen, logits_frozen), grads_frozen = grad_fn(
          params,
          inputs,
          attention_mask,
          decoder_input_ids,
          freeze_feature_encoder=True,
      )
  
      # ensure that the logits and losses remain precisely equal
      self.assertTrue((logits == logits_frozen).all())
      self.assertEqual(loss, loss_frozen)
  
      grads = flatten_dict(grads)
      grads_frozen = flatten_dict(grads_frozen)
  
      # ensure that the dicts of gradients contain the same keys
      self.assertEqual(grads.keys(), grads_frozen.keys())
  
      # ensure that the gradients of the feature extractor layers are precisely zero when frozen and contain non-zero entries when unfrozen
      feature_extractor_grads = tuple(
          grads[k] for k in grads if "feature_extractor" in k
      )
      feature_extractor_grads_frozen = tuple(
          grads_frozen[k]
          for k in grads_frozen
          if "feature_extractor" in k
      )
  
      for feature_extractor_grad, feature_extractor_grad_frozen in zip(
          feature_extractor_grads, feature_extractor_grads_frozen
      ):
          self.assertTrue((feature_extractor_grad_frozen == 0.0).all())
          self.assertTrue((feature_extractor_grad > 0.0).any())
  
      # ensure that the gradients of all unfrozen layers remain precisely equal, i.e. all layers excluding the frozen 'feature_extractor'
      grads = tuple(
          grads[k] for k in grads if "feature_extractor" not in k
      )
      grads_frozen = tuple(
          grads_frozen[k]
          for k in grads_frozen
          if "feature_extractor" not in k
      )
  
      for grad, grad_frozen in zip(grads, grads_frozen):
          self.assertTrue((grad == grad_frozen).all())
  
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 11 for calling the FlaxEncoderDecoderMixin obj's underlying member method code with example usage: obj.check_freeze_feature_encoder(config, inputs, attention_mask, encoder_hidden_states, decoder_config, decoder_input_ids, decoder_attention_mask) and returns: unknown, loss,  logits