code: def prepare_config_and_inputs(self):
      input_values = (
          tf.cast(
              ids_tensor([self.batch_size, self.seq_length], 32768),
              tf.float32,
          )
          / 32768.0
      )
      attention_mask = tf.ones_like(input_values)
  
      config = HubertConfig(
          hidden_size=self.hidden_size,
          feat_extract_norm=self.feat_extract_norm,
          feat_extract_dropout=self.feat_extract_dropout,
          feat_extract_activation=self.feat_extract_activation,
          conv_dim=self.conv_dim,
          conv_stride=self.conv_stride,
          conv_kernel=self.conv_kernel,
          conv_bias=self.conv_bias,
          num_conv_pos_embeddings=self.num_conv_pos_embeddings,
          num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
          num_hidden_layers=self.num_hidden_layers,
          num_attention_heads=self.num_attention_heads,
          hidden_dropout_prob=self.hidden_dropout_prob,
          intermediate_size=self.intermediate_size,
          layer_norm_eps=self.layer_norm_eps,
          hidden_act=self.hidden_act,
          initializer_range=self.initializer_range,
          vocab_size=self.vocab_size,
          do_stable_layer_norm=self.do_stable_layer_norm,
      )
  
      return config, input_values, attention_mask
  
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 2 for calling the TFHubertModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: config,  input_values,  attention_mask