code: def gaussian_state(cov, mu, hbar=2.0):
      r"""Returns a Gaussian state.
  
      This is simply a bare wrapper function,
      since the covariance matrix and means vector
      can be passed via the parameters unchanged.
  
      Note that both the covariance and means vector
      matrix should be in :math:`(\x_1,\dots, \x_N, \p_1, \dots, \p_N)`
      ordering.
  
      Args:
          cov (array): covariance matrix. Must be dimension :math:`2N\times 2N`,
              where N is the number of modes
          mu (array): vector means. Must be length-:math:`2N`,
              where N is the number of modes
          hbar (float): (default 2) the value of :math:`\hbar` in the commutation
              relation :math:`[\x,\p]=i\hbar`
  
      Returns:
          tuple: the mean and the covariance matrix of the Gaussian state
      """
      # pylint: disable=unused-argument
  
      # Note: the internal order of mu and cov is different to the one used in Strawberry Fields
      return cov, mu
  
imports: import math
  import cmath
  import numpy
  from scipy.special import factorial
  import pennylane
  from pennylane.ops import Identity
  from pennylane import Device
  from .._version import __version__
label_desc: Use image node_id 18 for calling a global function with example usage: gaussian_state(cov, mu, hbar) and returns: cov,  mu