code: def test_xlm_roberta_base(self):
      model = XLMRobertaModel.from_pretrained("xlm-roberta-base")
      input_ids = torch.tensor(
          [
              [
                  0,
                  581,
                  10269,
                  83,
                  99942,
                  136,
                  60742,
                  23,
                  70,
                  80583,
                  18276,
                  2,
              ]
          ]
      )
      # The dog is cute and lives in the garden house
  
      expected_output_shape = torch.Size(
          (1, 12, 768)
      )  # batch_size, sequence_length, embedding_vector_dim
      expected_output_values_last_dim = torch.tensor(
          [
              [
                  -0.0101,
                  0.1218,
                  -0.0803,
                  0.0801,
                  0.1327,
                  0.0776,
                  -0.1215,
                  0.2383,
                  0.3338,
                  0.3106,
                  0.0300,
                  0.0252,
              ]
          ]
      )
      #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
      #  xlmr.eval()
      #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
  
      output = model(input_ids)["last_hidden_state"].detach()
      self.assertEqual(output.shape, expected_output_shape)
      # compare the actual values for a slice of last dim
      self.assertTrue(
          torch.allclose(
              output[:, :, -1],
              expected_output_values_last_dim,
              atol=1e-3,
          )
      )
  
imports: import unittest
  from transformers import is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
label_desc: Use image node_id 1 for calling the XLMRobertaModelIntegrationTest obj's underlying member method code with example usage: obj.test_xlm_roberta_base() without return types