code: def _wfdict_to_statevector(wf_dict, norbs):
      r"""Convert a wavefunction in sparse dictionary format to a PennyLane statevector.
  
      In the sparse dictionary format, the keys ``(int_a, int_b)`` are integers whose binary
      representation shows the Fock occupation vector for alpha and beta electrons and values are the
      CI coefficients.
  
      Args:
          wf_dict (dict): the sparse dictionary format of a wavefunction
          norbs (int): number of molecular orbitals
  
      Returns:
          array: normalized state vector of length ``2**(number_of_spinorbitals)``
      """
      statevector = np.zeros(2 ** (2 * norbs), dtype=complex)
  
      for (int_a, int_b), coeff in wf_dict.items():
          bin_a = bin(int_a)[2:][::-1]
          bin_b = bin(int_b)[2:][::-1]
          bin_a += "0" * (norbs - len(bin_a))
          bin_b += "0" * (norbs - len(bin_b))
          bin_ab = "".join(i + j for i, j in zip(bin_a, bin_b))
          statevector[int(bin_ab, 2)] += coeff
  
      statevector = statevector / np.sqrt(np.sum(statevector**2))
  
      return statevector
  
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 11 for calling a global function with example usage: _wfdict_to_statevector(wf_dict, norbs) and returns: statevector