How to use the openfermion.ops._givens_rotations.swap_columns function in openfermion

# to put it into the form we want
    n = p // 2
    for i in range(1, n, 2):
        swap_rows(canonical, i, n + i - 1)
        swap_columns(canonical, i, n + i - 1)
        swap_columns(orthogonal, i, n + i - 1)
        if n % 2 != 0:
            swap_rows(canonical, n - 1, n + i)
            swap_columns(canonical, n - 1, n + i)
            swap_columns(orthogonal, n - 1, n + i)

    # Now we permute so that the upper right block is non-negative
    for i in range(n):
        if canonical[i, n + i] < 0.0:
            swap_rows(canonical, i, n + i)
            swap_columns(canonical, i, n + i)
            swap_columns(orthogonal, i, n + i)

    # Now we permute so that the nonzero entries are ordered by magnitude
    # We use insertion sort
    diagonal = canonical[range(n), range(n, 2 * n)]
    for i in range(n):
        # Insert the smallest element from the unsorted part of the list into
        # index i
        arg_min = numpy.argmin(diagonal[i:]) + i
        if arg_min != i:
            # Permute the upper right block
            swap_rows(canonical, i, arg_min)
            swap_columns(canonical, n + i, n + arg_min)
            swap_columns(orthogonal, n + i, n + arg_min)
            # Permute the lower left block
            swap_rows(canonical, n + i, n + arg_min)

raise ValueError('The input matrix must be antisymmetric.')

    # Compute Schur decomposition
    canonical, orthogonal = schur(antisymmetric_matrix, output='real')

    # The returned form is block diagonal; we need to permute rows and columns
    # to put it into the form we want
    n = p // 2
    for i in range(1, n, 2):
        swap_rows(canonical, i, n + i - 1)
        swap_columns(canonical, i, n + i - 1)
        swap_columns(orthogonal, i, n + i - 1)
        if n % 2 != 0:
            swap_rows(canonical, n - 1, n + i)
            swap_columns(canonical, n - 1, n + i)
            swap_columns(orthogonal, n - 1, n + i)

    # Now we permute so that the upper right block is non-negative
    for i in range(n):
        if canonical[i, n + i] < 0.0:
            swap_rows(canonical, i, n + i)
            swap_columns(canonical, i, n + i)
            swap_columns(orthogonal, i, n + i)

    # Now we permute so that the nonzero entries are ordered by magnitude
    # We use insertion sort
    diagonal = canonical[range(n), range(n, 2 * n)]
    for i in range(n):
        # Insert the smallest element from the unsorted part of the list into
        # index i
        arg_min = numpy.argmin(diagonal[i:]) + i
        if arg_min != i:

if not numpy.isclose(maxval, 0.0):
        raise ValueError('The input matrix must be antisymmetric.')

    # Compute Schur decomposition
    canonical, orthogonal = schur(antisymmetric_matrix, output='real')

    # The returned form is block diagonal; we need to permute rows and columns
    # to put it into the form we want
    n = p // 2
    for i in range(1, n, 2):
        swap_rows(canonical, i, n + i - 1)
        swap_columns(canonical, i, n + i - 1)
        swap_columns(orthogonal, i, n + i - 1)
        if n % 2 != 0:
            swap_rows(canonical, n - 1, n + i)
            swap_columns(canonical, n - 1, n + i)
            swap_columns(orthogonal, n - 1, n + i)

    # Now we permute so that the upper right block is non-negative
    for i in range(n):
        if canonical[i, n + i] < 0.0:
            swap_rows(canonical, i, n + i)
            swap_columns(canonical, i, n + i)
            swap_columns(orthogonal, i, n + i)

    # Now we permute so that the nonzero entries are ordered by magnitude
    # We use insertion sort
    diagonal = canonical[range(n), range(n, 2 * n)]
    for i in range(n):
        # Insert the smallest element from the unsorted part of the list into
        # index i
        arg_min = numpy.argmin(diagonal[i:]) + i

# Check that input matrix is antisymmetric
    matrix_plus_transpose = antisymmetric_matrix + antisymmetric_matrix.T
    maxval = numpy.max(numpy.abs(matrix_plus_transpose))
    if not numpy.isclose(maxval, 0.0):
        raise ValueError('The input matrix must be antisymmetric.')

    # Compute Schur decomposition
    canonical, orthogonal = schur(antisymmetric_matrix, output='real')

    # The returned form is block diagonal; we need to permute rows and columns
    # to put it into the form we want
    n = p // 2
    for i in range(1, n, 2):
        swap_rows(canonical, i, n + i - 1)
        swap_columns(canonical, i, n + i - 1)
        swap_columns(orthogonal, i, n + i - 1)
        if n % 2 != 0:
            swap_rows(canonical, n - 1, n + i)
            swap_columns(canonical, n - 1, n + i)
            swap_columns(orthogonal, n - 1, n + i)

    # Now we permute so that the upper right block is non-negative
    for i in range(n):
        if canonical[i, n + i] < 0.0:
            swap_rows(canonical, i, n + i)
            swap_columns(canonical, i, n + i)
            swap_columns(orthogonal, i, n + i)

    # Now we permute so that the nonzero entries are ordered by magnitude
    # We use insertion sort
    diagonal = canonical[range(n), range(n, 2 * n)]
    for i in range(n):

# Check that input matrix is antisymmetric
    matrix_plus_transpose = antisymmetric_matrix + antisymmetric_matrix.T
    maxval = numpy.max(numpy.abs(matrix_plus_transpose))
    if not numpy.isclose(maxval, 0.0):
        raise ValueError('The input matrix must be antisymmetric.')

    # Compute Schur decomposition
    canonical, orthogonal = schur(antisymmetric_matrix, output='real')

    # The returned form is block diagonal; we need to permute rows and columns
    # to put it into the form we want
    n = p // 2
    for i in range(1, n, 2):
        swap_rows(canonical, i, n + i - 1)
        swap_columns(canonical, i, n + i - 1)
        swap_columns(orthogonal, i, n + i - 1)
        if n % 2 != 0:
            swap_rows(canonical, n - 1, n + i)
            swap_columns(canonical, n - 1, n + i)
            swap_columns(orthogonal, n - 1, n + i)

    # Now we permute so that the upper right block is non-negative
    for i in range(n):
        if canonical[i, n + i] < 0.0:
            swap_rows(canonical, i, n + i)
            swap_columns(canonical, i, n + i)
            swap_columns(orthogonal, i, n + i)

    # Now we permute so that the nonzero entries are ordered by magnitude
    # We use insertion sort
    diagonal = canonical[range(n), range(n, 2 * n)]

# Now we permute so that the nonzero entries are ordered by magnitude
    # We use insertion sort
    diagonal = canonical[range(n), range(n, 2 * n)]
    for i in range(n):
        # Insert the smallest element from the unsorted part of the list into
        # index i
        arg_min = numpy.argmin(diagonal[i:]) + i
        if arg_min != i:
            # Permute the upper right block
            swap_rows(canonical, i, arg_min)
            swap_columns(canonical, n + i, n + arg_min)
            swap_columns(orthogonal, n + i, n + arg_min)
            # Permute the lower left block
            swap_rows(canonical, n + i, n + arg_min)
            swap_columns(canonical, i, arg_min)
            swap_columns(orthogonal, i, arg_min)
            # Update diagonal
            swap_rows(diagonal, i, arg_min)

    return canonical, orthogonal.T

# Now we permute so that the nonzero entries are ordered by magnitude
    # We use insertion sort
    diagonal = canonical[range(n), range(n, 2 * n)]
    for i in range(n):
        # Insert the smallest element from the unsorted part of the list into
        # index i
        arg_min = numpy.argmin(diagonal[i:]) + i
        if arg_min != i:
            # Permute the upper right block
            swap_rows(canonical, i, arg_min)
            swap_columns(canonical, n + i, n + arg_min)
            swap_columns(orthogonal, n + i, n + arg_min)
            # Permute the lower left block
            swap_rows(canonical, n + i, n + arg_min)
            swap_columns(canonical, i, arg_min)
            swap_columns(orthogonal, i, arg_min)
            # Update diagonal
            swap_rows(diagonal, i, arg_min)

    return canonical, orthogonal.T