code: def test_output_embeds_base_model(self):
      model = TFXLMRobertaModel.from_pretrained(
          "jplu/tf-xlm-roberta-base"
      )
  
      features = {
          "input_ids": tf.convert_to_tensor(
              [[0, 2646, 10269, 83, 99942, 2]], dtype=tf.int32
          ),  # "My dog is cute"
          "attention_mask": tf.convert_to_tensor(
              [[1, 1, 1, 1, 1, 1]], dtype=tf.int32
          ),
      }
  
      output = model(features)["last_hidden_state"]
      expected_shape = tf.TensorShape((1, 6, 768))
      self.assertEqual(output.shape, expected_shape)
      # compare the actual values for a slice.
      expected_slice = tf.convert_to_tensor(
          [
              [
                  [0.0681762, 0.10894451, 0.06772504],
                  [-0.06423668, 0.02366615, 0.04329344],
                  [-0.06057295, 0.09974135, -0.00070584],
              ]
          ],
          dtype=tf.float32,
      )
  
      self.assertTrue(
          np.allclose(
              output[:, :3, :3].numpy(),
              expected_slice.numpy(),
              atol=1e-4,
          )
      )
  
imports: import unittest
  from transformers import is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
label_desc: Use image node_id 1 for calling the TFFlaubertModelIntegrationTest obj's underlying member method code with example usage: obj.test_output_embeds_base_model() without return types