code: def smape(y_true, y_pred):
      r"""Compute the Symmetric Mean Absolute Percentage Error.
  
      The symmetric mean absolute percentage error (SMAPE) is an accuracy measure
      based on percentage (or relative) errors. Defined as follows:
  
          :math:`\frac{100\%}{n}\sum_{t=1}^{n}{\frac{|F_{t}-A_{t}|}{
          (|A_{t}|+|F_{t}|)/2}}`
  
      Where a perfect SMAPE score is 0.0, and a higher score indicates a higher
      error rate.
  
      Parameters
      ----------
      y_true : array-like, shape=(n_samples,)
          The true test values of y.
  
      y_pred : array-like, shape=(n_samples,)
          The forecasted values of y.
  
      Examples
      --------
      A typical case:
      >>> import numpy as np
      >>> y_true = np.array([0.07533, 0.07533, 0.07533, 0.07533,
      ...                    0.07533, 0.07533, 0.0672, 0.0672])
      >>> y_pred = np.array([0.102, 0.107, 0.047, 0.1,
      ...                    0.032, 0.047, 0.108, 0.089])
      >>> smape(y_true, y_pred)
      42.60306631890196
  
      A perfect score:
      >>> smape(y_true, y_true)
      0.0
  
      References
      ----------
      .. [1] https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error
      """  # noqa: E501
      y_true = check_endog(
          y_true,
          copy=False,
          preserve_series=False,
      )
      y_pred = check_endog(
          y_pred,
          copy=False,
          preserve_series=False,
      )
      abs_diff = np.abs(y_pred - y_true)
      return np.mean(
          (abs_diff * 200 / (np.abs(y_pred) + np.abs(y_true)))
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: smape(y_true, y_pred) and returns: np