code: def test_inference_no_head(self):
      model = TFLEDForConditionalGeneration.from_pretrained(
          "allenai/led-base-16384"
      ).led
  
      # change to intended input here
      input_ids = _long_tensor(
          [512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]
      )
      decoder_input_ids = _long_tensor(
          [128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]
      )
      inputs_dict = prepare_led_inputs_dict(
          model.config, input_ids, decoder_input_ids
      )
      output = model(**inputs_dict)[0]
      expected_shape = (1, 1024, 768)
      self.assertEqual(output.shape, expected_shape)
      # change to expected output here
      expected_slice = tf.convert_to_tensor(
          [
              [2.3050, 2.8279, 0.6531],
              [-1.8457, -0.1455, -3.5661],
              [-1.0186, 0.4586, -2.2043],
          ],
      )
      tf.debugging.assert_near(
          output[:, :3, :3], expected_slice, atol=1e-3
      )
  
imports: import unittest
  from transformers import LEDConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow, tooslow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
label_desc: Use image node_id 1 for calling the TFLEDModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_no_head() without return types