code: def __init__(
      self,
      logical_operator,
      weight_concentration_prior_type,
      weight_concentration_,
      means_,
      covariances_,
      covariance_type,
      degrees_of_freedom_,
      mean_precision_,
      device,
  ):
      super(BayesianGaussianMixture, self).__init__(
          logical_operator, regression=True
      )
  
      if weight_concentration_prior_type == "dirichlet_process":
          self.weight_concentration_prior_type = (
              weight_concentration_prior_type
          )
      else:
          raise RuntimeError(
              "Unsupported weight_concentration_prior_type: {0}".format(
                  weight_concentration_prior_type
              )
          )
  
      if covariance_type == "full":
          self.covariance_type = covariance_type
      else:
          raise RuntimeError(
              "Unsupported covariance_type: {0}".format(covariance_type)
          )
  
      self.weight_concentration_ = torch.nn.Parameter(
          torch.FloatTensor(weight_concentration_), requires_grad=False
      )
      self.means_ = torch.nn.Parameter(
          torch.FloatTensor(means_), requires_grad=False
      )
      precisions_cholesky_ = _compute_precision_cholesky(
          covariances_, covariance_type
      )
      self.precisions_cholesky_ = torch.nn.Parameter(
          torch.FloatTensor(precisions_cholesky_), requires_grad=False
      )
      self.degrees_of_freedom_ = torch.nn.Parameter(
          torch.FloatTensor(degrees_of_freedom_), requires_grad=False
      )
      self.mean_precision_ = torch.nn.Parameter(
          torch.FloatTensor(mean_precision_), requires_grad=False
      )
  
file_path: hummingbird/hummingbird/ml/operator_converters/_mixture_implementations.py
imports: import torch
  import torch.nn
  from ._physical_operator import PhysicalOperator
label_desc: Use image node_id 1 to create a new BayesianGaussianMixture object from inherited base classes: PhysicalOperator, torch.nn with example: obj = BayesianGaussianMixture(logical_operator, weight_concentration_prior_type, weight_concentration_, means_, covariances_, covariance_type, degrees_of_freedom_, mean_precision_, device)