code: def test_basic(self):
      input_ids = torch.tensor(
          [[4, 10]], dtype=torch.long, device=torch_device
      )
      emb1 = SinusoidalPositionalEmbedding(
          num_positions=6, embedding_dim=6, padding_idx=self.padding_idx
      ).to(torch_device)
      emb = emb1(input_ids)
      desired_weights = torch.tensor(
          [
              [
                  9.0930e-01,
                  1.9999e-02,
                  2.0000e-04,
                  -4.1615e-01,
                  9.9980e-01,
                  1.0000e00,
              ],
              [
                  1.4112e-01,
                  2.9995e-02,
                  3.0000e-04,
                  -9.8999e-01,
                  9.9955e-01,
                  1.0000e00,
              ],
          ]
      ).to(torch_device)
      self.assertTrue(
          torch.allclose(emb[0], desired_weights, atol=self.tolerance),
          msg=f"\nexp:\n{desired_weights}\ngot:\n{emb[0]}\n",
      )
  
imports: import tempfile
  import unittest
  import timeout_decorator
  from parameterized import parameterized
  from transformers import FSMTConfig, is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, require_torch_fp16, slow, torch_device
  from transformers.utils import cached_property
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the TestSinusoidalPositionalEmbeddings obj's underlying member method code with example usage: obj.test_basic() without return types