code: def median_squared_error(
      y_true,
      y_pred,
      horizon_weight=None,
      multioutput="uniform_average",
      square_root=False,
      **kwargs,
  ):
      """Median squared error (MdSE) or root median squared error (RMdSE).
  
      If `square_root` is False then calculates MdSE and if `square_root` is True
      then RMdSE is calculated. Both MdSE and RMdSE return non-negative floating
      point. The best value is 0.0.
  
      Like MSE, MdSE is measured in squared units of the input data. RMdSE is
      on the same scale as the input data like RMSE. Because MdSE and RMdSE
      square the forecast error rather than taking the absolute value, they
      penalize large errors more than MAE or MdAE.
  
      Taking the median instead of the mean of the squared errors makes
      this metric more robust to error outliers relative to a meean based metric
      since the median tends to be a more robust measure of central tendency in
      the presence of outliers.
  
      Parameters
      ----------
      y_true : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
               where fh is the forecasting horizon
          Ground truth (correct) target values.
  
      y_pred : pd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) \
               where fh is the forecasting horizon
          Forecasted values.
  
      horizon_weight : array-like of shape (fh,), default=None
          Forecast horizon weights.
  
      multioutput : {'raw_values', 'uniform_average'}  or array-like of shape \
              (n_outputs,), default='uniform_average'
          Defines how to aggregate metric for multivariate (multioutput) data.
          If array-like, values used as weights to average the errors.
          If 'raw_values', returns a full set of errors in case of multioutput input.
          If 'uniform_average', errors of all outputs are averaged with uniform weight.
  
      square_root : bool, default=False
          Whether to take the square root of the mean squared error.
          If True, returns root mean squared error (RMSE)
          If False, returns mean squared error (MSE)
  
      Returns
      -------
      loss : float
          MdSE loss.
          If multioutput is 'raw_values', then MdSE is returned for each
          output separately.
          If multioutput is 'uniform_average' or an ndarray of weights, then the
          weighted average MdSE of all output errors is returned.
  
      See Also
      --------
      mean_absolute_error
      median_absolute_error
      mean_squared_error
      geometric_mean_absolute_error
      geometric_mean_squared_error
  
      References
      ----------
      Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of
      forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
  
      Examples
      --------
      >>> from sktime.performance_metrics.forecasting import median_squared_error
      >>> y_true = np.array([3, -0.5, 2, 7, 2])
      >>> y_pred = np.array([2.5, 0.0, 2, 8, 1.25])
      >>> median_squared_error(y_true, y_pred)
      0.25
      >>> median_squared_error(y_true, y_pred, square_root=True)
      0.5
      >>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
      >>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
      >>> median_squared_error(y_true, y_pred)
      0.625
      >>> median_squared_error(y_true, y_pred, square_root=True)
      0.75
      >>> median_squared_error(y_true, y_pred, multioutput='raw_values')
      array([0.25, 1.  ])
      >>> median_squared_error(y_true, y_pred, multioutput='raw_values', square_root=True)
      array([0.5, 1. ])
      >>> median_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])
      0.7749999999999999
      >>> median_squared_error(y_true, y_pred, multioutput=[0.3, 0.7], square_root=True)
      0.85
      """
      _, y_true, y_pred, multioutput = _check_reg_targets(
          y_true, y_pred, multioutput
      )
      if horizon_weight is None:
          output_errors = np.median(np.square(y_pred - y_true), axis=0)
  
      else:
          check_consistent_length(y_true, horizon_weight)
          output_errors = _weighted_percentile(
              np.square(y_pred - y_true), sample_weight=horizon_weight
          )
  
      if square_root:
          output_errors = np.sqrt(output_errors)
  
      if isinstance(multioutput, str):
          if multioutput == "raw_values":
              return output_errors
          elif multioutput == "uniform_average":
              # pass None as weights to np.average: uniform mean
              multioutput = None
  
      return np.average(output_errors, weights=multioutput)
  
imports: import numpy
  from scipy.stats import gmean
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_error
  from sklearn.metrics import median_absolute_error
  from sklearn.metrics._regression import _check_reg_targets
  from sklearn.utils.stats import _weighted_percentile
  from sklearn.utils.validation import check_consistent_length
  from sktime.utils.stats import _weighted_geometric_mean
label_desc: Use image node_id 11 for calling a global function with example usage: median_squared_error(y_true, y_pred, horizon_weight, multioutput, square_root) and returns: np, output_errors