code: def curvelinear_test1(fig):
      """Grid for custom transform."""
  
      def tr(x, y):
          return np.sign(x) * abs(x) ** 0.5, y
  
      def inv_tr(x, y):
          return np.sign(x) * x**2, y
  
      grid_helper = GridHelperCurveLinear(
          (tr, inv_tr),
          extreme_finder=ExtremeFinderSimple(20, 20),
          # better tick density
          grid_locator1=MaxNLocator(nbins=6),
          grid_locator2=MaxNLocator(nbins=6),
      )
  
      ax1 = fig.add_subplot(axes_class=Axes, grid_helper=grid_helper)
      # ax1 will have a ticks and gridlines defined by the given
      # transform (+ transData of the Axes). Note that the transform of the Axes
      # itself (i.e., transData) is not affected by the given transform.
  
      ax1.imshow(
          np.arange(25).reshape(5, 5),
          vmax=50,
          cmap=plt.cm.gray_r,
          origin="lower",
      )
  
imports: import matplotlib.pyplot
  import numpy
  from mpl_toolkits.axisartist.axislines import Axes
  from mpl_toolkits.axisartist.grid_finder import ExtremeFinderSimple, MaxNLocator
  from mpl_toolkits.axisartist.grid_helper_curvelinear import GridHelperCurveLinear
label_desc: Use image node_id 1 for calling a global function with example usage: curvelinear_test1(fig) and returns: unknown,  y, unknown,  y