How to use the tensornetwork.connect function in tensornetwork

def _apply_op_network(site_edges, op, n1, pbc=False):
  N = len(site_edges)
  op_sites = len(op.shape) // 2
  n_op = tensornetwork.Node(op, backend="tensorflow")
  for m in range(op_sites):
    target_site = (n1 + m) % N if pbc else n1 + m
    tensornetwork.connect(n_op[op_sites + m], site_edges[target_site])
    site_edges[target_site] = n_op[m]
  return site_edges, n_op

def _add_edge(self, node1, idx1, node2, idx2):
        """Adds an edge to the underlying tensor network.

        The edge is also added to ``self._edges`` for bookkeeping.

        Args:
            node1 (tn.Node): first node to connect
            idx1 (int): index of node1 to add the edge to
            node2 (tn.Node): second node to connect
            idx2 (int): index of node2 to add the edge to

        Returns:
            tn.Edge: the newly created edge
        """
        edge = tn.connect(node1[idx1], node2[idx2])
        self._edges.append(edge)

        return edge

"""
  env = env_dis(hamiltonian, state, isometry, disentangler)

  nenv = tensornetwork.Node(
    env, axis_names=["bl", "br", "tl", "tr"], backend="jax")
  output_edges = [nenv["bl"], nenv["br"], nenv["tl"], nenv["tr"]]

  nu, _, nv, _ = tensornetwork.split_node_full_svd(
    nenv,
    [nenv["bl"], nenv["br"]],
    [nenv["tl"], nenv["tr"]],
    left_edge_name="s1",
    right_edge_name="s2")
  nu["s1"].disconnect()
  nv["s2"].disconnect()
  tensornetwork.connect(nu["s1"], nv["s2"])
  nres = tensornetwork.contract_between(nu, nv, output_edge_order=output_edges)

  return np.conj(nres.get_tensor())

tensornetwork.connect(iso_c[0], un_l[3])
    tensornetwork.connect(iso_c[1], un_r[2])
    tensornetwork.connect(iso_r[0], un_r[3])
    tensornetwork.connect(iso_r[1], iso_r_con[1])

    if dirn == 'right':
      tensornetwork.connect(un_l[0], un_l_con[0])
      tensornetwork.connect(un_l[1], op[3])
      tensornetwork.connect(un_r[0], op[4])
      tensornetwork.connect(un_r[1], op[5])
      tensornetwork.connect(op[0], un_l_con[1])
      tensornetwork.connect(op[1], un_r_con[0])
      tensornetwork.connect(op[2], un_r_con[1])
    elif dirn == 'left':
      tensornetwork.connect(un_l[0], op[3])
      tensornetwork.connect(un_l[1], op[4])
      tensornetwork.connect(un_r[0], op[5])
      tensornetwork.connect(un_r[1], un_r_con[1])
      tensornetwork.connect(op[0], un_l_con[0])
      tensornetwork.connect(op[1], un_l_con[1])
      tensornetwork.connect(op[2], un_r_con[0])

    tensornetwork.connect(un_l_con[2], iso_l_con[1])
    tensornetwork.connect(un_l_con[3], iso_c_con[0])
    tensornetwork.connect(un_r_con[2], iso_c_con[1])
    tensornetwork.connect(un_r_con[3], iso_r_con[0])

    tensornetwork.connect(iso_l_con[2], rho[3])
    tensornetwork.connect(iso_c_con[2], rho[4])
    tensornetwork.connect(iso_r_con[2], rho[5])

    # FIXME: Check that this is giving us a good path!

tensornetwork.connect(iso_l[0], iso_l_con[0])
    tensornetwork.connect(iso_l[1], un_l[2])
    tensornetwork.connect(iso_c[0], un_l[3])
    tensornetwork.connect(iso_c[1], un_r[2])
    tensornetwork.connect(iso_r[0], un_r[3])
    tensornetwork.connect(iso_r[1], iso_r_con[1])

    if dirn == 'right':
      tensornetwork.connect(un_l[0], un_l_con[0])
      tensornetwork.connect(un_l[1], op[3])
      tensornetwork.connect(un_r[0], op[4])
      tensornetwork.connect(un_r[1], op[5])
      tensornetwork.connect(op[0], un_l_con[1])
      tensornetwork.connect(op[1], un_r_con[0])
      tensornetwork.connect(op[2], un_r_con[1])
    elif dirn == 'left':
      tensornetwork.connect(un_l[0], op[3])
      tensornetwork.connect(un_l[1], op[4])
      tensornetwork.connect(un_r[0], op[5])
      tensornetwork.connect(un_r[1], un_r_con[1])
      tensornetwork.connect(op[0], un_l_con[0])
      tensornetwork.connect(op[1], un_l_con[1])
      tensornetwork.connect(op[2], un_r_con[0])

    tensornetwork.connect(un_l_con[2], iso_l_con[1])
    tensornetwork.connect(un_l_con[3], iso_c_con[0])
    tensornetwork.connect(un_r_con[2], iso_c_con[1])
    tensornetwork.connect(un_r_con[3], iso_r_con[0])

    tensornetwork.connect(iso_l_con[2], rho[3])
    tensornetwork.connect(iso_c_con[2], rho[4])

identified with site `0`. Otherwise, site `N-1` has no neighbors to the
        right.

    Returns:
      expval: The expectation value.
  """
  n_psi = tensornetwork.Node(psi, backend="tensorflow")
  site_edges = n_psi.get_all_edges()

  site_edges, n_op = _apply_op_network(site_edges, op, n1, pbc)

  n_op_psi = n_op @ n_psi

  n_psi_conj = tensornetwork.Node(tf.math.conj(psi), backend="tensorflow")
  for i in range(len(site_edges)):
    tensornetwork.connect(site_edges[i], n_psi_conj[i])

  res = n_psi_conj @ n_op_psi

  return res.tensor

The updated isometry.
  """
  env = env_iso(hamiltonian, state, isometry, disentangler)

  nenv = tensornetwork.Node(env, axis_names=["l", "r", "t"], backend="jax")
  output_edges = [nenv["l"], nenv["r"], nenv["t"]]

  nu, _, nv, _ = tensornetwork.split_node_full_svd(
    nenv,
    [nenv["l"], nenv["r"]],
    [nenv["t"]],
    left_edge_name="s1",
    right_edge_name="s2")
  nu["s1"].disconnect()
  nv["s2"].disconnect()
  tensornetwork.connect(nu["s1"], nv["s2"])
  nres = tensornetwork.contract_between(nu, nv, output_edge_order=output_edges)

  return np.conj(nres.get_tensor())