code: def create_and_check_batch_inference(
      self, config, input_values, *args
  ):
      # test does not pass for models making use of `group_norm`
      # check: https://github.com/pytorch/fairseq/issues/3227
      config.layerdrop = 0.0
      model = TFHubertModel(config)
  
      input_values = input_values[:3]
      attention_mask = tf.ones_like(input_values)
  
      input_lengths = tf.constant(
          [input_values.shape[-1] // i for i in [4, 2, 1]]
      )
      length_mask = tf.sequence_mask(input_lengths, dtype=tf.float32)
  
      # convert values that are over input_lengths to padding
      input_values = input_values * length_mask
      attention_mask = attention_mask * length_mask
  
      batch_outputs = model(
          input_values, attention_mask=attention_mask, training=False
      ).last_hidden_state
  
      for i in range(input_values.shape[0]):
          input_slice = input_values[i : i + 1, : input_lengths[i]]
          output = model(input_slice, training=False).last_hidden_state
  
          batch_output = batch_outputs[i : i + 1, : output.shape[1]]
          self.parent.assertTrue(
              np.allclose(output, batch_output, atol=1e-3)
          )
  
imports: import copy
  import inspect
  import math
  import unittest
  import numpy
  import pytest
  from transformers import is_tf_available
  from transformers.testing_utils import require_soundfile, require_tf, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
label_desc: Use image node_id 4 for calling the TFHubertModelTester obj's underlying member method code with example usage: obj.create_and_check_batch_inference(config, input_values) without return types