code: def test_change_basis_real_to_complex_j_2(self):
      # Test for j = 2, which means we have a 5x5 transformation matrix
      j = 2
      Q = equivariance_utils.change_basis_real_to_complex(
          j, dtype=torch.complex64
      )
      expected_Q = torch.tensor(
          [
              [
                  0.0000 + 0.7071j,
                  -0.0000 + 0.0000j,
                  -0.0000 + 0.0000j,
                  -0.0000 + 0.0000j,
                  -0.7071 + 0.0000j,
              ],
              [
                  -0.0000 + 0.0000j,
                  0.0000 + 0.7071j,
                  -0.0000 + 0.0000j,
                  -0.7071 + 0.0000j,
                  -0.0000 + 0.0000j,
              ],
              [
                  -0.0000 + 0.0000j,
                  -0.0000 + 0.0000j,
                  -1.0000 + 0.0000j,
                  -0.0000 + 0.0000j,
                  -0.0000 + 0.0000j,
              ],
              [
                  -0.0000 + 0.0000j,
                  0.0000 + 0.7071j,
                  -0.0000 + 0.0000j,
                  0.7071 - 0.0000j,
                  -0.0000 + 0.0000j,
              ],
              [
                  -0.0000 - 0.7071j,
                  -0.0000 + 0.0000j,
                  -0.0000 + 0.0000j,
                  -0.0000 + 0.0000j,
                  -0.7071 + 0.0000j,
              ],
          ],
          dtype=torch.complex64,
      )
      self.assertTrue(torch.allclose(Q, expected_Q))
  
file_path: deepchem/deepchem/utils/test/test_equivariance_utils.py
imports: import unittest
label_desc: Use image node_id 6 for calling the TestEquivarianceUtils obj's underlying member method code with example usage: obj.test_change_basis_real_to_complex_j_2() without return types