code: def convert_sklearn_BayesianGaussianMixture(
      operator, device, extra_config
  ):
      """
      Converter for `sklearn.mixture.BayesianGaussianMixture`
  
      Args:
          operator: An operator wrapping a `sklearn.mixture.BayesianGaussianMixture` model
          device: String defining the type of device the converted operator should be run on
          extra_config: Extra configuration used to select the best conversion strategy
  
      Returns:
          A PyTorch model
      """
      assert operator is not None, "Cannot convert None operator"
  
      weight_concentration_prior_type = (
          operator.raw_operator.weight_concentration_prior_type
      )
      weight_concentration_ = np.array(
          operator.raw_operator.weight_concentration_
      )
      means_ = operator.raw_operator.means_
      covariances_ = operator.raw_operator.covariances_
      covariance_type = operator.raw_operator.covariance_type
      degrees_of_freedom_ = operator.raw_operator.degrees_of_freedom_
      mean_precision_ = operator.raw_operator.mean_precision_
  
      return BayesianGaussianMixture(
          operator,
          weight_concentration_prior_type,
          weight_concentration_,
          means_,
          covariances_,
          covariance_type,
          degrees_of_freedom_,
          mean_precision_,
          device,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: convert_sklearn_BayesianGaussianMixture(operator, device, extra_config) and returns: BayesianGaussianMixture