code: def prepare_config_and_inputs(self):
      model_tester_encoder = Wav2Vec2ModelTester(self, batch_size=13)
      model_tester_decoder = Speech2Text2StandaloneDecoderModelTester(
          self, batch_size=13, d_model=32, max_position_embeddings=512
      )
      encoder_config_and_inputs = (
          model_tester_encoder.prepare_config_and_inputs()
      )
      decoder_config_and_inputs = (
          model_tester_decoder.prepare_config_and_inputs()
      )
      (
          config,
          input_values,
          input_mask,
      ) = encoder_config_and_inputs
      (
          decoder_config,
          decoder_input_ids,
          decoder_attention_mask,
          _,
      ) = decoder_config_and_inputs
  
      # make sure that cross attention layers are added
      decoder_config.add_cross_attention = True
      #  disable cache for now
      decoder_config.use_cache = False
      return {
          "config": config,
          "input_values": input_values,
          "attention_mask": input_mask,
          "decoder_config": decoder_config,
          "decoder_input_ids": decoder_input_ids,
          "decoder_attention_mask": decoder_attention_mask,
          "labels": decoder_input_ids,
      }
  
imports: import tempfile
  import unittest
  from transformers import is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
  from ..bert.test_modeling_bert import BertModelTester
  from ..speech_to_text.test_modeling_speech_to_text import Speech2TextModelTester
  from ..speech_to_text_2.test_modeling_speech_to_text_2 import Speech2Text2StandaloneDecoderModelTester
  from ..wav2vec2.test_modeling_wav2vec2 import Wav2Vec2ModelTester
label_desc: Use image node_id 2 for calling the Wav2Vec2Speech2Text2 obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: dict