text: Title: Custom metric for LinearRegression.gridsearch
  Body: Hi!
  
  I would like to pass this custom metric to gridsearch:
  
  ```
  def asymmetric_custom_metric(y_true, y_pred, penalization_factor = 5):
      """
      Custom loss function that penalizes predictions below the true value more than predictions above the value.
  
      Parameters:
          y_true (ndarray): Array of true values.
          y_pred (ndarray): Array of predicted values.
  
      Returns:
          float: Custom loss value.
  
      """
  
      # Calculate the difference between true and predicted values
      diff = (y_pred - y_true).astype(float)
  
      # Calculate the loss
      loss = np.where(diff < 0, np.square(y_true - y_pred) * (1 + penalization_factor) , np.square(y_true - y_pred))
  
      # Calculate the average loss
      avg_loss = np.mean(loss)
      return avg_loss
  ```
  
  But gridsearch only accepts metrics comming directly from darts.metrics.
  
  There is a Wrapper to transform sklearn or custom metrics into a darts metric?
  
  Thanks!
  Brian.
  