code: def _shci_state(wavefunction, tol=1e-15):
      r"""Construct a wavefunction from the SHCI wavefunction obtained from the Dice 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 are typically stored under
      SHCI.outputfile.
  
      Args:
          wavefunction tuple(list[str], array[float]): determinants and coefficients in chemist 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 pyscf.shciscf import shci
      >>> import numpy as np
      >>> mol = gto.M(atom=[['Li', (0, 0, 0)], ['Li', (0,0,0.71)]], basis='sto6g', symmetry="d2h")
      >>> myhf = scf.RHF(mol).run()
      >>> ncas, nelecas_a, nelecas_b = mol.nao, mol.nelectron // 2, mol.nelectron // 2
      >>> myshci = mcscf.CASCI(myhf, ncas, (nelecas_a, nelecas_b))
      >>> output_file = f"shci_output.out"
      >>> myshci.fcisolver = shci.SHCI(myhf.mol)
      >>> myshci.fcisolver.outputFile = output_file
      >>> e_tot, e_ci, ss, mo_coeff, mo_energies = myshci.kernel(verbose=5)
      >>> (dets, coeffs) = [post-process shci_output.out to get tuple of
          dets (list of strs) and coeffs (list of floats)]
      >>> wf_shci = _shci_state((dets, coeffs), tol=1e-1)
      >>> print(wf_shci)
      {(7, 7): 0.8874167069, (11, 11): -0.3075774156, (19, 19): -0.3075774156, (35, 35): -0.1450474361}
      """
  
      dets, coefs = wavefunction
  
      xa = []
      xb = []
      dat = []
  
      for coef, det in zip(coefs, dets):
          if abs(coef) > tol:
              bin_a, bin_b = _sitevec_to_fock(list(det), "shci")
  
              xa.append(bin_a)
              xb.append(bin_b)
              dat.append(coef)
  
      ## create the FCI matrix as a dict
      dict_fcimatr = dict(zip(list(zip(xa, xb)), 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 17 for calling a global function with example usage: _shci_state(wavefunction, tol) and returns: dict_fcimatr