code: def _compute(self, X, reference_distribution):
      # convert to numpy arrays
      X = np.array(X)
      reference_distribution = np.array(reference_distribution)
  
      # Assert that arrays are 2D
      if len(X.shape) != 2:
          raise ValueError("Expected `X` to be a 2D vector")
      if len(reference_distribution.shape) != 2:
          raise ValueError(
              "Expected `reference_distribution` to be a 2D vector"
          )
      if reference_distribution.shape[0] < 2:
          raise ValueError(
              "Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension"
          )
  
      # Get mahalanobis distance for each prediction
      X_minus_mu = X - np.mean(reference_distribution)
      cov = np.cov(reference_distribution.T)
      try:
          inv_covmat = np.linalg.inv(cov)
      except np.linalg.LinAlgError:
          inv_covmat = np.linalg.pinv(cov)
      left_term = np.dot(X_minus_mu, inv_covmat)
      mahal_dist = np.dot(left_term, X_minus_mu.T).diagonal()
  
      return {"mahalanobis": mahal_dist}
  
imports: import numpy
  import datasets
label_desc: Use image node_id 2 for calling the Mahalanobis obj's underlying member method code with example usage: obj._compute(X, reference_distribution) and returns: dict