code: def test_particular_case_1_with_adaptive_false():
      # Verify that symbolic expressions and numerical lambda functions are
      # evaluated with the same algorithm. In particular, uniform evaluation
      # is going to use np.vectorize, which correctly evaluates the following
      # mathematical function.
      if not np:
          skip("numpy not installed.")
  
      def do_test(a, b):
          d1 = a.get_data()
          d2 = b.get_data()
          for t, v in zip(d1, d2):
              assert np.allclose(t, v)
  
      n = symbols("n")
      a = S(2) / 3
      epsilon = 0.01
      xn = (n**3 + n**2) ** (S(1) / 3) - (n**3 - n**2) ** (
          S(1) / 3
      )
      expr = Abs(xn - a) - epsilon
      math_func = lambdify([n], expr)
  
      s3 = LineOver1DRangeSeries(
          expr, (n, -10, 10), "", adaptive=False, n=10
      )
      s4 = LineOver1DRangeSeries(
          math_func, ("n", -10, 10), "", adaptive=False, n=10
      )
      do_test(s3, s4)
  
imports: from sympy import latex, exp, symbols, I, pi, sin, cos, tan, log, sqrt, re, im, arg, frac, Sum, S, Abs, lambdify, Function, dsolve, Eq, floor, Tuple
  from sympy.external import import_module
  from sympy.plotting.series import LineOver1DRangeSeries, Parametric2DLineSeries, Parametric3DLineSeries, SurfaceOver2DRangeSeries, ContourSeries, ParametricSurfaceSeries, ImplicitSeries, _set_discretization_points, List2DSeries
  from sympy.testing.pytest import raises, warns, XFAIL, skip, ignore_warnings
label_desc: Use image node_id 32 for calling a global function with example usage: test_particular_case_1_with_adaptive_false() without return types