code: def test_basic(self):
      input_ids = torch.tensor(
          [[4, 10]], dtype=torch.long, device=torch_device
      )
      emb1 = RoFormerSinusoidalPositionalEmbedding(
          num_positions=6, embedding_dim=6
      ).to(torch_device)
      emb = emb1(input_ids.shape)
      desired_weights = torch.tensor(
          [
              [0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000],
              [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000],
          ]
      ).to(torch_device)
      self.assertTrue(
          torch.allclose(emb, desired_weights, atol=self.tolerance),
          msg=f"\nexp:\n{desired_weights}\ngot:\n{emb[0]}\n",
      )
  
imports: import unittest
  from transformers import RoFormerConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 1 for calling the RoFormerSinusoidalPositionalEmbeddingTest obj's underlying member method code with example usage: obj.test_basic() without return types