code: def test_freeze_feature_encoder(self):
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
  
      input_values = inputs_dict["input_values"]
      attention_mask = inputs_dict["attention_mask"]
  
      model = FlaxWav2Vec2ForPreTraining(config)
      params = model.params
  
      # dummy loss function
      def compute_loss(
          params,
          input_values,
          attention_mask,
          freeze_feature_encoder: bool = False,
          epsilon: float = 1e-8,
      ):
          outputs = model(
              input_values,
              attention_mask=attention_mask,
              freeze_feature_encoder=freeze_feature_encoder,
              params=params,
          )
          # compute cosine similarity of projected and projected_quantized states
          cosine_sim = optax.cosine_similarity(
              outputs.projected_states,
              outputs.projected_quantized_states,
              epsilon=epsilon,
          )
          loss = cosine_sim.sum()
          return loss, outputs.to_tuple()
  
      # transform the loss function to get the gradients
      grad_fn = jax.value_and_grad(compute_loss, has_aux=True)
  
      # compute loss, outputs and gradients for unfrozen model
      (loss, outputs), grads = grad_fn(
          params,
          input_values,
          attention_mask,
          freeze_feature_encoder=False,
      )
  
      # compare to loss, outputs and gradients for frozen model
      (loss_frozen, outputs_frozen), grads_frozen = grad_fn(
          params,
          input_values,
          attention_mask,
          freeze_feature_encoder=True,
      )
  
      # ensure that the outputs and losses remain precisely equal
      for output, output_frozen in zip(outputs, outputs_frozen):
          self.assertTrue((output == output_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 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 inspect
  import math
  import multiprocessing
  import os
  import traceback
  import unittest
  import numpy
  from datasets import load_dataset
  from transformers import Wav2Vec2Config, is_flax_available
  from transformers.testing_utils import CaptureLogger, is_flaky, is_librosa_available, is_pt_flax_cross_test, is_pyctcdecode_available, require_flax, require_librosa, require_pyctcdecode, require_soundfile, run_test_in_subprocess, slow
  from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, random_attention_mask
label_desc: Use image node_id 5 for calling the FlaxWav2Vec2ModelTest obj's underlying member method code with example usage: obj.test_freeze_feature_encoder() and returns: loss,  outputs