code: def create_axes(title, figsize=(16, 6)):
      fig = plt.figure(figsize=figsize)
      fig.suptitle(title)
  
      # define the axis for the first plot
      left, width = 0.1, 0.22
      bottom, height = 0.1, 0.7
      bottom_h = height + 0.15
      left_h = left + width + 0.02
  
      rect_scatter = [left, bottom, width, height]
      rect_histx = [left, bottom_h, width, 0.1]
      rect_histy = [left_h, bottom, 0.05, height]
  
      ax_scatter = plt.axes(rect_scatter)
      ax_histx = plt.axes(rect_histx)
      ax_histy = plt.axes(rect_histy)
  
      # define the axis for the zoomed-in plot
      left = width + left + 0.2
      left_h = left + width + 0.02
  
      rect_scatter = [left, bottom, width, height]
      rect_histx = [left, bottom_h, width, 0.1]
      rect_histy = [left_h, bottom, 0.05, height]
  
      ax_scatter_zoom = plt.axes(rect_scatter)
      ax_histx_zoom = plt.axes(rect_histx)
      ax_histy_zoom = plt.axes(rect_histy)
  
      # define the axis for the colorbar
      left, width = width + left + 0.13, 0.01
  
      rect_colorbar = [left, bottom, width, height]
      ax_colorbar = plt.axes(rect_colorbar)
  
      return (
          (ax_scatter, ax_histy, ax_histx),
          (ax_scatter_zoom, ax_histy_zoom, ax_histx_zoom),
          ax_colorbar,
      )
  
file_path: scikit-learn/examples/preprocessing/plot_all_scaling.py
imports: import matplotlib
  import numpy
  from matplotlib import cm
  from matplotlib import pyplot
  from sklearn.datasets import fetch_california_housing
  from sklearn.preprocessing import MaxAbsScaler, MinMaxScaler, Normalizer, PowerTransformer, QuantileTransformer, RobustScaler, StandardScaler, minmax_scale
label_desc: Use image node_id 1 for calling a global function with example usage: create_axes(title, figsize) and returns: ax_scatter,  ax_histy,  ax_histx,  ax_scatter_zoom,  ax_histy_zoom,  ax_histx_zoom,  ax_colorbar