code: def test_train(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)
  
      features_shape = (
          input_values.shape[0],
          model._get_feat_extract_output_lengths(
              np.array(input_values.shape[1])
          ),
      )
  
      batch_size, sequence_length = features_shape[:2]
  
      mask_prob = 0.5
      mask_length = 4
      mask_time_indices = _compute_mask_indices(
          (batch_size, sequence_length), mask_prob, mask_length
      )
  
      dropout_rng, gumbel_rng = jax.random.split(jax.random.PRNGKey(0))
  
      output = model(
          input_values,
          attention_mask=attention_mask,
          mask_time_indices=mask_time_indices,
          train=True,
          dropout_rng=dropout_rng,
          gumbel_rng=gumbel_rng,
      )[0]
  
      self.assertTrue(
          output.shape
          == (
              batch_size,
              sequence_length,
              model.config.proj_codevector_dim,
          )
      )
  
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 2 for calling the FlaxWav2Vec2ModelTest obj's underlying member method code with example usage: obj.test_train() without return types