code: def sMAPE(y_true, y_pred, multioutput="raw_values"):
      """
      Calculate Symmetric mean absolute percentage error (sMAPE).
  
      .. math::
  
          \\text{sMAPE} = \\frac{100\%}{n} \\sum_{t=1}^n \\frac{|y_t-\\hat{y_t}|}{|y_t|+|\\hat{y_t}|}
  
      :param y_true: Array-like of shape = (n_samples, \*).
             Ground truth (correct) target values.
      :param y_pred: Array-like of shape = (n_samples, \*).
             Estimated target values.
      :param multioutput: String in ['raw_values', 'uniform_average']
  
      :return: Float or ndarray of floats.
               A non-negative floating point value (the best value is 0.0), or an
               array of floating point values, one for each individual target.
      """
      y_true, y_pred, original_shape = _standardize_input(
          y_true, y_pred, multioutput
      )
      output_errors = np.mean(
          100
          * np.abs(y_true - y_pred)
          / (np.abs(y_true) + np.abs(y_pred) + EPSILON),
          axis=0,
      )
      if multioutput == "raw_values":
          return output_errors.reshape(original_shape)
      return np.mean(output_errors)
  
imports: from sklearn.metrics import mean_squared_error
  from sklearn.metrics import r2_score
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_log_error
  import numpy
  import pandas
label_desc: Use image node_id 2 for calling a global function with example usage: sMAPE(y_true, y_pred, multioutput) and returns: np, output_errors