code: def symmetry_reduce(tensors, structure, tol=1e-8, **kwargs):
      
      sga = SpacegroupAnalyzer(structure, **kwargs)
      symmops = sga.get_symmetry_operations(cartesian=True)
      unique_mapping = TensorMapping([tensors[0]], [[]], tol=tol)
      for tensor in tensors[1:]:
          is_unique = True
          for unique_tensor, symmop in itertools.product(unique_mapping, symmops):
              if np.allclose(unique_tensor.transform(symmop), tensor, atol=tol):
                  unique_mapping[unique_tensor].append(symmop)
                  is_unique = False
                  break
          if is_unique:
              unique_mapping[tensor] = []
      return unique_mapping
docs: Function that converts a list of tensors corresponding to a structure
      and returns a dictionary consisting of unique tensor keys with symmop
      values corresponding to transformations that will result in derivative
      tensors from the original list
  
      Args:
          tensors (list of tensors): list of Tensor objects to test for
              symmetrically-equivalent duplicates
          structure (Structure): structure from which to get symmetry
          tol (float): tolerance for tensor equivalence
          kwargs: keyword arguments for the SpacegroupAnalyzer
  
      returns:
          dictionary consisting of unique tensors with symmetry operations
          corresponding to those which will reconstruct the remaining
          tensors as values