How to use the tensornetwork.contract_between function in tensornetwork

if skip > 0:
        if gate is not None:
          raise ValueError(
              "Overlapping gates in same layer at site {}!".format(n))
        skip -= 1
      elif gate is not None:
        site_edges, n_gate = _apply_op_network(site_edges, gate, n)
        nodes.append(n_gate)

        # keep track of how many sites this gate included
        op_sites = len(gate.shape) // 2
        skip = op_sites - 1

  # NOTE: This may not be the optimal order if transpose costs are considered.
  n_psi = reduce(tensornetwork.contract_between, nodes)
  n_psi.reorder_edges(site_edges)

  return n_psi.tensor

for obs_node, obs_wires in zip(obs_nodes, wires):
            meas_wires.extend(obs_wires)
            for idx, w in enumerate(obs_wires):
                output_idx = idx
                input_idx = len(obs_wires) + idx
                self._add_edge(obs_node, input_idx, ket, w)  # A|psi>
                self._add_edge(bra, w, obs_node, output_idx)  #  tolerance:
            warnings.warn(
                "Nonvanishing imaginary part {} in expectation value.".format(expval.imag),
                RuntimeWarning,
            )
        return self._real(expval)

# [output_idx1, output_idx2, ..., input_idx1, input_idx2, ...]
        for obs_node, obs_wires in zip(obs_nodes, wires):
            meas_wires.extend(obs_wires)
            for idx, w in enumerate(obs_wires):
                output_idx = idx
                input_idx = len(obs_wires) + idx
                self._add_edge(obs_node, input_idx, ket, w)  # A|psi>
                self._add_edge(bra, w, obs_node, output_idx)  #  tolerance:
            warnings.warn(
                "Nonvanishing imaginary part {} in expectation value.".format(expval.imag),
                RuntimeWarning,
            )
        return self._real(expval)

"""
  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())