code: def _compute_log_det_cholesky(
      matrix_chol, covariance_type, n_features
  ):
      if covariance_type == "full":
          n_components, _, _ = matrix_chol.shape
          log_det_chol = torch.sum(
              torch.log(
                  matrix_chol.reshape(n_components, -1)[
                      :, :: n_features + 1
                  ]
              ),
              1,
          )
          return log_det_chol
      else:
          raise NotImplementedError(
              "Hummingbird does not currently support {} covariance type for BayesianGaussianMixture. The supported covariance type is full.".format(
                  covariance_type
              )
          )
  
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 for calling a global function with example usage: _compute_log_det_cholesky(matrix_chol, covariance_type, n_features) and returns: log_det_chol