code: def create_and_check_model_as_decoder(
      self,
      config,
      input_ids,
      input_mask,
      sequence_labels,
      token_labels,
      choice_labels,
      encoder_hidden_states,
      encoder_attention_mask,
  ):
      config.add_cross_attention = True
  
      model = TFEsmModel(config=config)
      inputs = {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "encoder_hidden_states": encoder_hidden_states,
          "encoder_attention_mask": encoder_attention_mask,
      }
      result = model(inputs)
  
      inputs = [input_ids, input_mask]
      result = model(
          inputs, encoder_hidden_states=encoder_hidden_states
      )
  
      # Also check the case where encoder outputs are not passed
      result = model(input_ids, attention_mask=input_mask)
  
      self.parent.assertEqual(
          result.last_hidden_state.shape,
          (self.batch_size, self.seq_length, self.hidden_size),
      )
  
imports: import unittest
  from transformers import EsmConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 5 for calling the TFEsmModelTester obj's underlying member method code with example usage: obj.create_and_check_model_as_decoder(config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask) without return types