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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def symmetry_reduce(tensors, structure, tol=1e-8, **kwargs):
 """ 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 """

    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