code: def prepare_config_and_inputs_for_common(self):
      input_ids = ids_tensor(
          [self.batch_size, self.seq_length - 1], self.vocab_size
      )
      eos_tensor = tf.expand_dims(
          tf.constant([self.eos_token_id] * self.batch_size), 1
      )
      input_ids = tf.concat([input_ids, eos_tensor], axis=1)
  
      decoder_input_ids = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      )
  
      config = self.config_cls(
          vocab_size=self.vocab_size,
          d_model=self.hidden_size,
          encoder_layers=self.num_hidden_layers,
          decoder_layers=self.num_hidden_layers,
          encoder_attention_heads=self.num_attention_heads,
          decoder_attention_heads=self.num_attention_heads,
          encoder_ffn_dim=self.intermediate_size,
          decoder_ffn_dim=self.intermediate_size,
          dropout=self.hidden_dropout_prob,
          attention_dropout=self.attention_probs_dropout_prob,
          max_position_embeddings=self.max_position_embeddings,
          eos_token_ids=[2],
          bos_token_id=self.bos_token_id,
          pad_token_id=self.pad_token_id,
          decoder_start_token_id=self.pad_token_id,
          **self.config_updates,
      )
      inputs_dict = prepare_mbart_inputs_dict(
          config, input_ids, decoder_input_ids
      )
      return config, inputs_dict
  
imports: from __future__ import annotations
  import unittest
  from transformers import AutoTokenizer, MBartConfig, is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the TFMBartModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs_for_common() and returns: config,  inputs_dict