code: def test_adaptive():
      # verify that adaptive-related keywords produces the expected results
      if not np:
          skip("numpy not installed.")
  
      x, y = symbols("x, y")
  
      s1 = LineOver1DRangeSeries(
          sin(x), (x, -10, 10), "", adaptive=True, depth=2
      )
      x1, _ = s1.get_data()
      s2 = LineOver1DRangeSeries(
          sin(x), (x, -10, 10), "", adaptive=True, depth=5
      )
      x2, _ = s2.get_data()
      s3 = LineOver1DRangeSeries(
          sin(x), (x, -10, 10), "", adaptive=True
      )
      x3, _ = s3.get_data()
      assert len(x1) < len(x2) < len(x3)
  
      s1 = Parametric2DLineSeries(
          cos(x), sin(x), (x, 0, 2 * pi), adaptive=True, depth=2
      )
      (
          x1,
          _,
          _,
      ) = s1.get_data()
      s2 = Parametric2DLineSeries(
          cos(x), sin(x), (x, 0, 2 * pi), adaptive=True, depth=5
      )
      x2, _, _ = s2.get_data()
      s3 = Parametric2DLineSeries(
          cos(x), sin(x), (x, 0, 2 * pi), adaptive=True
      )
      x3, _, _ = s3.get_data()
      assert len(x1) < len(x2) < len(x3)
  
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 1 for calling a global function with example usage: test_adaptive() without return types