code: def erf(x: np.ndarray) -> np.ndarray:
      a = np.abs(x)
  
      case_nan = np.isnan(x)
      case_posinf = np.isposinf(x)
      case_neginf = np.isneginf(x)
      case_tiny = a < 2**-28
      case_small1 = (2**-28 <= a) & (a < 0.84375)
      case_small2 = (0.84375 <= a) & (a < 1.25)
      case_med1 = (1.25 <= a) & (a < 1 / 0.35)
      case_med2 = (1 / 0.35 <= a) & (a < 6)
      case_big = a >= 6
  
      def calc_case_tiny(x: np.ndarray) -> np.ndarray:
          return x + efx * x
  
      def calc_case_small1(x: np.ndarray) -> np.ndarray:
          z = x * x
          r = pp(z)
          s = qq(z)
          y = r / s
          return x + x * y
  
      def calc_case_small2(x: np.ndarray) -> np.ndarray:
          s = np.abs(x) - one
          P = pa(s)
          Q = qa(s)
          absout = erx + P / Q
          return absout * np.sign(x)
  
      def calc_case_med1(x: np.ndarray) -> np.ndarray:
          sign = np.sign(x)
          x = np.abs(x)
          s = one / (x * x)
          R = ra(s)
          S = sa(s)
          # the following 3 lines are omitted for the following reasons:
          # (1) there are no easy way to implement SET_LOW_WORD equivalent method in NumPy
          # (2) we don't need very high accuracy in our use case.
          # z = x
          # SET_LOW_WORD(z, 0)
          # r = np.exp(-z * z - 0.5625) * np.exp((z - x) * (z + x) + R / S)
          r = np.exp(-x * x - 0.5625) * np.exp(R / S)
          return (one - r / x) * sign
  
      def calc_case_med2(x: np.ndarray) -> np.ndarray:
          sign = np.sign(x)
          x = np.abs(x)
          s = one / (x * x)
          R = rb(s)
          S = sb(s)
          # z = x
          # SET_LOW_WORD(z, 0)
          # r = np.exp(-z * z - 0.5625) * np.exp((z - x) * (z + x) + R / S)
          r = np.exp(-x * x - 0.5625) * np.exp(R / S)
          return (one - r / x) * sign
  
      def calc_case_big(x: np.ndarray) -> np.ndarray:
          return np.sign(x)
  
      out = np.full_like(a, fill_value=np.nan, dtype=np.float64)
      out[case_nan] = np.nan
      out[case_posinf] = 1.0
      out[case_neginf] = -1.0
      if x[case_tiny].size:
          out[case_tiny] = calc_case_tiny(x[case_tiny])
      if x[case_small1].size:
          out[case_small1] = calc_case_small1(x[case_small1])
      if x[case_small2].size:
          out[case_small2] = calc_case_small2(x[case_small2])
      if x[case_med1].size:
          out[case_med1] = calc_case_med1(x[case_med1])
      if x[case_med2].size:
          out[case_med2] = calc_case_med2(x[case_med2])
      if x[case_big].size:
          out[case_big] = calc_case_big(x[case_big])
  
      return out
  
label_desc: Use image node_id 1 for calling a global function with example usage: erf(x) and returns: out, unknown, unknown, unknown, unknown, unknown, np