code: def _interp_evaluate(coefficients, t0, t1, t):
      """Evaluate polynomial interpolation at the given time point.
  
      Args:
          coefficients: list of Tensor coefficients as created by `interp_fit`.
          t0: scalar float64 Tensor giving the start of the interval.
          t1: scalar float64 Tensor giving the end of the interval.
          t: scalar float64 Tensor giving the desired interpolation point.
  
      Returns:
          Polynomial interpolation of the coefficients at time `t`.
      """
  
      assert (t0 <= t) & (
          t <= t1
      ), "invalid interpolation, fails `t0 <= t <= t1`: {}, {}, {}".format(
          t0, t, t1
      )
      x = (t - t0) / (t1 - t0)
      x = x.to(coefficients[0].dtype)
  
      total = coefficients[0] + x * coefficients[1]
      x_power = x
      for coefficient in coefficients[2:]:
          x_power = x_power * x
          total = total + x_power * coefficient
  
      return total
  
label_desc: Use image node_id 2 for calling a global function with example usage: _interp_evaluate(coefficients, t0, t1, t) and returns: total