How to use the tensornetwork.ncon function in tensornetwork

[-1, -2, 9, 10], [4, 8, 5], [4, 10, 6]]
    indList3 = [[3, 9, 2, 4], [1, 5, 2], [1, 8, 3], [7, -3, 5, 6],
                [7, -1, 8, 10], [-4, 6, 4], [-2, 10, 9]]
    indList4 = [[3, 6, 2, 5], [-3, 1, 2], [-1, 1, 3], [-4, 4, 5], [-2, 4, 6]]

    rhoABout = 0.5 * tn.ncon([
        rhoBA, w_isometry,
        tf.conj(w_isometry), unitary,
        tf.conj(unitary), v_isometry,
        tf.conj(v_isometry)
    ], indList1)

    if refsym:
        rhoABout = rhoABout + tf.transpose(rhoABout, (1, 0, 3, 2))
    else:
        rhoABout = rhoABout + 0.5 * tn.ncon([
            rhoBA, w_isometry,
            tf.conj(w_isometry), unitary,
            tf.conj(unitary), v_isometry,
            tf.conj(v_isometry)
        ], indList3)

    rhoBAout = 0.5 * tn.ncon([
        rhoBA, w_isometry,
        tf.conj(w_isometry), unitary,
        tf.conj(unitary), v_isometry,
        tf.conj(v_isometry)
    ], indList2)

    rhoBAout = rhoBAout + 0.5 * tn.ncon([
        rhoAB, v_isometry,
        tf.conj(v_isometry), w_isometry,

rhoBA (tf.Tensor):  reduced densit matrix on b-a lattice
        w_isometry (tf.Tensor): MERA isometry
        v_isometry (tf.Tensor): MERA isometry
        unitary (tf.Tensor):    MERA disentangler
        refsym (bool): if true, enforce reflection symmetry
    Returns:
        tf.Tensor 
    """
    
    for n in range(nsteps):
        rhoAB, rhoBA = descending_super_operator(rhoAB, rhoBA, w_isometry,
                                                 v_isometry, unitary, refsym)
        rhoAB = 1 / 2 * (rhoAB + tf.conj(tf.transpose(
            rhoAB, (2, 3, 0, 1)))) / tn.ncon([rhoAB], [[1, 2, 1, 2]])
        rhoBA = 1 / 2 * (rhoBA + tf.conj(tf.transpose(
            rhoBA, (2, 3, 0, 1)))) / tn.ncon([rhoBA], [[1, 2, 1, 2]])
        if refsym:
            rhoAB = 0.5 * rhoAB + 0.5 * tf.transpose(rhoAB, (1, 0, 3, 2))
            rhoBA = 0.5 * rhoBA + 0.5 * tf.transpose(rhoBA, (1, 0, 3, 2))
    return rhoAB, rhoBA

def ncon(self, tensors, network_structure, *args, **kwargs):
    return tensornetwork.ncon(
      tensors, network_structure, *args, **kwargs, backend=self.name)

def get_amplitude(self, sigmas):
      """
      compute the amplitude of configuration `sigma`
      This is not very efficient
      Args:
        sigma (tf.Tensor of shape (n_samples, N):  basis configuration
      Returns:
        tf.Tensor of shape (n_samples): the amplitudes

      """
      ds = self.d
      dtype = self.dtype
      left = tf.expand_dims(tf.ones(shape=(sigmas.shape[0], self.D[0]),dtype=dtype), 1)  #(Nt, 1, Dl)

      for site in range(len(self)):
        tmp = tn.ncon([self.get_tensor(site), tf.one_hot(sigmas[:,site], ds[site], dtype=dtype)],[[-2, 1, -3], [-1, 1]]) #(Nt, Dl, Dr)
        left = tf.matmul(left, tmp) #(Nt, 1, Dr)
      return tf.squeeze(left)

unitary (tf.Tensor):  disentangler
        refsym (bool):          if true, enforce reflection symmetry
    Returns:
        rhoABout (tf.Tensor): descended reduced density matrix on A-B lattice
        rhoBAout (tf.Tensor): descended reduced density matrix on B-A lattice
    """

    indList1 = [[9, 3, 4, 2], [-3, 5, 4], [-1, 10, 9], [-4, 7, 5, 6],
                [-2, 7, 10, 8], [1, 6, 2], [1, 8, 3]]
    indList2 = [[3, 6, 2, 5], [1, 7, 2], [1, 9, 3], [-3, -4, 7, 8],
                [-1, -2, 9, 10], [4, 8, 5], [4, 10, 6]]
    indList3 = [[3, 9, 2, 4], [1, 5, 2], [1, 8, 3], [7, -3, 5, 6],
                [7, -1, 8, 10], [-4, 6, 4], [-2, 10, 9]]
    indList4 = [[3, 6, 2, 5], [-3, 1, 2], [-1, 1, 3], [-4, 4, 5], [-2, 4, 6]]

    rhoABout = 0.5 * tn.ncon([
        rhoBA, w_isometry,
        tf.conj(w_isometry), unitary,
        tf.conj(unitary), v_isometry,
        tf.conj(v_isometry)
    ], indList1)

    if refsym:
        rhoABout = rhoABout + tf.transpose(rhoABout, (1, 0, 3, 2))
    else:
        rhoABout = rhoABout + 0.5 * tn.ncon([
            rhoBA, w_isometry,
            tf.conj(w_isometry), unitary,
            tf.conj(unitary), v_isometry,
            tf.conj(v_isometry)
        ], indList3)

indList2 = [[3, 4, 1, 2], [5, 6, -3], [5, 7, -1], [1, 2, 6, 9],
                [3, 4, 7, 10], [8, 9, -4], [8, 10, -2]]
    indList3 = [[5, 7, 2, 1], [8, 9, -3], [8, 10, -1], [4, 2, 9, 3],
                [4, 5, 10, 6], [1, 3, -4], [7, 6, -2]]
    indList4 = [[3, 6, 2, 5], [2, 1, -3], [3, 1, -1], [5, 4, -4], [6, 4, -2]]

    hamBAout = tn.ncon([
        hamAB, w_isometry,
        tf.conj(w_isometry), unitary,
        tf.conj(unitary), v_isometry,
        tf.conj(v_isometry)
    ], indList1)
    if refsym:
        hamBAout = hamBAout + tf.transpose(hamBAout, (1, 0, 3, 2))
    else:
        hamBAout = hamBAout + tn.ncon([
            hamAB, w_isometry,
            tf.conj(w_isometry), unitary,
            tf.conj(unitary), v_isometry,
            tf.conj(v_isometry)
        ], indList3)

    hamBAout = hamBAout + tn.ncon([
        hamBA, w_isometry,
        tf.conj(w_isometry), unitary,
        tf.conj(unitary), v_isometry,
        tf.conj(v_isometry)
    ], indList2)
    hamABout = tn.ncon([
        hamBA, v_isometry,
        tf.conj(v_isometry), w_isometry,
        tf.conj(w_isometry)

indList1 = [[7, 8, 10, -1], [4, 3, 9, 2], [10, -3, 9], [7, 5, 4],
                [8, -2, 5, 6], [1, -4, 2], [1, 6, 3]]
    indList2 = [[7, 8, -1, -2], [3, 6, 2, 5], [1, -3, 2], [1, 9, 3],
                [7, 8, 9, 10], [4, -4, 5], [4, 10, 6]]
    indList3 = [[7, 8, -2, 10], [3, 4, 2, 9], [1, -3, 2], [1, 5, 3],
                [-1, 7, 5, 6], [10, -4, 9], [8, 6, 4]]

    uEnv = tn.ncon(
        [hamAB, rhoBA, w,
         tf.conj(w), tf.conj(u), v,
         tf.conj(v)], indList1)
    if refsym:
        uEnv = uEnv + tf.transpose(uEnv, (1, 0, 3, 2))
    else:
        uEnv = uEnv + tn.ncon(
            [hamAB, rhoBA, w,
             tf.conj(w), tf.conj(u), v,
             tf.conj(v)], indList3)

    uEnv = uEnv + tn.ncon(
        [hamBA, rhoBA, w,
         tf.conj(w), tf.conj(u), v,
         tf.conj(v)], indList2)

    return uEnv

def w_update_svd(wIn):
    """
    obtain the update to the isometry using tf.tensor
    """
    shape = wIn.shape
    st, ut, vt = tf.linalg.svd(
        tf.reshape(wIn, (shape[0] * shape[1], shape[2])), full_matrices=False)
    return -tf.reshape(tn.ncon([ut, tf.conj(vt)], [[-1, 1], [-2, 1]]), shape)

def w_update_svd_numpy(wIn):
    """
    obtain the update to the isometry using numpy svd
    """
    shape = wIn.shape
    ut, st, vt = np.linalg.svd(
        tf.reshape(wIn, (shape[0] * shape[1], shape[2])), full_matrices=False)
    return -tf.reshape(tn.ncon([ut, vt], [[-1, 1], [1, -2]]), shape)

def u_update_svd(wIn):
    """
    obtain the update to the disentangler using tf.svd
    """
    shape = wIn.shape
    st, ut, vt = tf.linalg.svd(
        tf.reshape(wIn, (shape[0] * shape[1], shape[2] * shape[3])),
        full_matrices=False)
    return -tf.reshape(tn.ncon([ut, tf.conj(vt)], [[-1, 1], [-2, 1]]), shape)