code: def _dmrg_state(wavefunction, tol=1e-15):
      r"""Construct a wavefunction from the DMRG wavefunction obtained from the Block2 library.
  
      The generated wavefunction is a dictionary where the keys represent a configuration, which
      corresponds to a Slater determinant, and the values are the CI coefficients of the Slater
      determinant. Each dictionary key is a tuple of two integers. The binary representation of these
      integers correspond to a specific configuration: the first number represents the
      configuration of the alpha electrons and the second number represents the configuration of the
      beta electrons. For instance, the Hartree-Fock state :math:`|1 1 0 0 \rangle` will be
      represented by the flipped binary string ``0011`` which is split to ``01`` and ``01`` for
      the alpha and beta electrons. The integer corresponding to ``01`` is ``1`` and the dictionary
      representation of the Hartree-Fock state will be ``{(1, 1): 1.0}``. The dictionary
      representation of a state with ``0.99`` contribution from the Hartree-Fock state and ``0.01``
      contribution from the doubly-excited state, i.e., :math:`|0 0 1 1 \rangle`, will be
      ``{(1, 1): 0.99, (2, 2): 0.01}``.
  
      The determinants and coefficients should be supplied externally. They should be calculated by
      using Block2 DMRGDriver's `get_csf_coefficients()` method.
  
      Args:
          wavefunction tuple(list[int], array[float]): determinants and coefficients in physicist notation
          tol (float): the tolerance for discarding Slater determinants with small coefficients
  
      Returns:
          dict: dictionary of the form `{(int_a, int_b) : coeff}`, with integers `int_a, int_b`
          having binary representation corresponding to the Fock occupation vector in alpha and beta
          spin sectors, respectively, and coeff being the CI coefficients of those configurations
  
      **Example**
  
      >>> from pyscf import gto, scf, mcscf
      >>> from pyblock2.driver.core import DMRGDriver, SymmetryTypes
      >>> from pyblock2._pyscf.ao2mo import integrals as itg
      >>> mol = gto.M(atom=[['H', (0, 0, 0)], ['H', (0,0,0.71)]], basis='sto3g')
      >>> mf = scf.RHF(mol).run()
      >>> mc = mcscf.CASCI(mf, 2, 2)
      >>> ncas, n_elec, spin, ecore, h1e, g2e, orb_sym = itg.get_rhf_integrals(mf, mc.ncore, mc.ncas, g2e_symm=8)
      >>> driver = DMRGDriver(scratch="./tmp", symm_type=SymmetryTypes.SU2)
      >>> driver.initialize_system(n_sites=ncas, n_elec=n_elec, spin=spin, orb_sym=orb_sym)
      >>> mpo = driver.get_qc_mpo(h1e=h1e, g2e=g2e, ecore=ecore)
      >>> ket = driver.get_random_mps(tag="GS")
      >>> wavefunction = driver.get_csf_coefficients(ket)
      >>> wf_dmrg = _dmrg_state(wavefunction, tol=1e-1)
      >>> print(wf_dmrg)
      {(2, 2): 0.6995876453534665, (1, 2): 0.7021101425511932, (1, 1): 0.13273460059658818}
      """
      dets, coeffs = wavefunction
  
      row, col, dat = [], [], []
  
      for ii, det in enumerate(dets):
          stra, strb = _sitevec_to_fock(det, format="dmrg")
          row.append(stra)
          col.append(strb)
  
          # compute and fix parity to stick to pyscf notation
          lsta = np.array(list(map(int, bin(stra)[2:])))[::-1]
          lstb = np.array(list(map(int, bin(strb)[2:])))[::-1]
  
          # pad the relevant list
          maxlen = max([len(lsta), len(lstb)])
          lsta = np.pad(lsta, (0, maxlen - len(lsta)))
          lstb = np.pad(lstb, (0, maxlen - len(lstb)))
  
          which_occ = np.where(lsta == 1)[0]
          parity = (-1) ** np.sum(
              [np.sum(lstb[: int(ind)]) for ind in which_occ]
          )
          dat.append(parity * coeffs[ii])
  
      ## create the FCI matrix as a dict
      dict_fcimatr = dict(zip(list(zip(row, col)), dat))
  
      # filter based on tolerance cutoff
      dict_fcimatr = {
          key: value
          for key, value in dict_fcimatr.items()
          if abs(value) > tol
      }
  
      return dict_fcimatr
  
imports: import warnings
  from itertools import product
  import numpy
  import pennylane
  from pennylane.operation import Tensor, active_new_opmath
  from pennylane.pauli import pauli_sentence
  from pennylane.wires import Wires
label_desc: Use image node_id 15 for calling a global function with example usage: _dmrg_state(wavefunction, tol) and returns: dict_fcimatr