How to use the tensornetwork.conj function in tensornetwork

isometry: The isometry tensor (rank 3) of the binary MERA.
    disentangler: The disentangler tensor (rank 4) of the binary MERA.

  Returns:
    The energy.
  """
  backend = "jax"

  out = []
  for dirn in ('left', 'right'):
    iso_l = tensornetwork.Node(isometry, backend=backend)
    iso_c = tensornetwork.Node(isometry, backend=backend)
    iso_r = tensornetwork.Node(isometry, backend=backend)

    iso_l_con = tensornetwork.conj(iso_l)
    iso_c_con = tensornetwork.conj(iso_c)
    iso_r_con = tensornetwork.conj(iso_r)

    op = tensornetwork.Node(hamiltonian, backend=backend)
    rho = tensornetwork.Node(state, backend=backend)

    un_l = tensornetwork.Node(disentangler, backend=backend)
    un_l_con = tensornetwork.conj(un_l)

    un_r = tensornetwork.Node(disentangler, backend=backend)
    un_r_con = tensornetwork.conj(un_r)

    tensornetwork.connect(iso_l[2], rho[0])
    tensornetwork.connect(iso_c[2], rho[1])
    tensornetwork.connect(iso_r[2], rho[2])

    tensornetwork.connect(iso_l[0], iso_l_con[0])

iso_l = tensornetwork.Node(isometry, backend=backend)
    iso_c = tensornetwork.Node(isometry, backend=backend)
    iso_r = tensornetwork.Node(isometry, backend=backend)

    iso_l_con = tensornetwork.conj(iso_l)
    iso_c_con = tensornetwork.conj(iso_c)
    iso_r_con = tensornetwork.conj(iso_r)

    op = tensornetwork.Node(hamiltonian, backend=backend)
    rho = tensornetwork.Node(state, backend=backend)

    un_l = tensornetwork.Node(disentangler, backend=backend)
    un_l_con = tensornetwork.conj(un_l)

    un_r = tensornetwork.Node(disentangler, backend=backend)
    un_r_con = tensornetwork.conj(un_r)

    tensornetwork.connect(iso_l[2], rho[0])
    tensornetwork.connect(iso_c[2], rho[1])
    tensornetwork.connect(iso_r[2], rho[2])

    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])

def ev(self, obs_nodes, wires):
        r"""Expectation value of observables on specified wires.

         Args:
            obs_nodes (Sequence[tn.Node]): the observables as tensornetwork Nodes
            wires (Sequence[Sequence[int]]): measured subsystems for each observable
         Returns:
            float: expectation value :math:`\expect{A} = \bra{\psi}A\ket{\psi}`
        """

        all_wires = tuple(w for w in range(self.num_wires))
        ket = self._add_node(self._state_node, wires=all_wires, name="Ket")
        bra = self._add_node(tn.conj(ket), wires=all_wires, name="Bra")
        meas_wires = []
        # We need to build up  step-by-step.
        # For wires which are measured, we need to connect edges between
        # bra, obs_node, and ket.
        # For wires which are not measured, we need to connect edges between
        # bra and ket.
        # We use the convention that the indices of a tensor are ordered like
        # [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)  # 

MERA layer.
    isometry: The isometry tensor (rank 3) of the binary MERA.
    disentangler: The disentangler tensor (rank 4) of the binary MERA.

  Returns:
    The energy.
  """
  backend = "jax"

  out = []
  for dirn in ('left', 'right'):
    iso_l = tensornetwork.Node(isometry, backend=backend)
    iso_c = tensornetwork.Node(isometry, backend=backend)
    iso_r = tensornetwork.Node(isometry, backend=backend)

    iso_l_con = tensornetwork.conj(iso_l)
    iso_c_con = tensornetwork.conj(iso_c)
    iso_r_con = tensornetwork.conj(iso_r)

    op = tensornetwork.Node(hamiltonian, backend=backend)
    rho = tensornetwork.Node(state, backend=backend)

    un_l = tensornetwork.Node(disentangler, backend=backend)
    un_l_con = tensornetwork.conj(un_l)

    un_r = tensornetwork.Node(disentangler, backend=backend)
    un_r_con = tensornetwork.conj(un_r)

    tensornetwork.connect(iso_l[2], rho[0])
    tensornetwork.connect(iso_c[2], rho[1])
    tensornetwork.connect(iso_r[2], rho[2])

disentangler: The disentangler tensor (rank 4) of the binary MERA.

  Returns:
    The energy.
  """
  backend = "jax"

  out = []
  for dirn in ('left', 'right'):
    iso_l = tensornetwork.Node(isometry, backend=backend)
    iso_c = tensornetwork.Node(isometry, backend=backend)
    iso_r = tensornetwork.Node(isometry, backend=backend)

    iso_l_con = tensornetwork.conj(iso_l)
    iso_c_con = tensornetwork.conj(iso_c)
    iso_r_con = tensornetwork.conj(iso_r)

    op = tensornetwork.Node(hamiltonian, backend=backend)
    rho = tensornetwork.Node(state, backend=backend)

    un_l = tensornetwork.Node(disentangler, backend=backend)
    un_l_con = tensornetwork.conj(un_l)

    un_r = tensornetwork.Node(disentangler, backend=backend)
    un_r_con = tensornetwork.conj(un_r)

    tensornetwork.connect(iso_l[2], rho[0])
    tensornetwork.connect(iso_c[2], rho[1])
    tensornetwork.connect(iso_r[2], rho[2])

    tensornetwork.connect(iso_l[0], iso_l_con[0])
    tensornetwork.connect(iso_l[1], un_l[2])

out = []
  for dirn in ('left', 'right'):
    iso_l = tensornetwork.Node(isometry, backend=backend)
    iso_c = tensornetwork.Node(isometry, backend=backend)
    iso_r = tensornetwork.Node(isometry, backend=backend)

    iso_l_con = tensornetwork.conj(iso_l)
    iso_c_con = tensornetwork.conj(iso_c)
    iso_r_con = tensornetwork.conj(iso_r)

    op = tensornetwork.Node(hamiltonian, backend=backend)
    rho = tensornetwork.Node(state, backend=backend)

    un_l = tensornetwork.Node(disentangler, backend=backend)
    un_l_con = tensornetwork.conj(un_l)

    un_r = tensornetwork.Node(disentangler, backend=backend)
    un_r_con = tensornetwork.conj(un_r)

    tensornetwork.connect(iso_l[2], rho[0])
    tensornetwork.connect(iso_c[2], rho[1])
    tensornetwork.connect(iso_r[2], rho[2])

    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':