code: def plot_distribution(
      axes, X, y, hist_nbins=50, title="", x0_label="", x1_label=""
  ):
      ax, hist_X1, hist_X0 = axes
  
      ax.set_title(title)
      ax.set_xlabel(x0_label)
      ax.set_ylabel(x1_label)
  
      # The scatter plot
      colors = cmap(y)
      ax.scatter(
          X[:, 0], X[:, 1], alpha=0.5, marker="o", s=5, lw=0, c=colors
      )
  
      # Removing the top and the right spine for aesthetics
      # make nice axis layout
      ax.spines["top"].set_visible(False)
      ax.spines["right"].set_visible(False)
      ax.get_xaxis().tick_bottom()
      ax.get_yaxis().tick_left()
      ax.spines["left"].set_position(("outward", 10))
      ax.spines["bottom"].set_position(("outward", 10))
  
      # Histogram for axis X1 (feature 5)
      hist_X1.set_ylim(ax.get_ylim())
      hist_X1.hist(
          X[:, 1],
          bins=hist_nbins,
          orientation="horizontal",
          color="grey",
          ec="grey",
      )
      hist_X1.axis("off")
  
      # Histogram for axis X0 (feature 0)
      hist_X0.set_xlim(ax.get_xlim())
      hist_X0.hist(
          X[:, 0],
          bins=hist_nbins,
          orientation="vertical",
          color="grey",
          ec="grey",
      )
      hist_X0.axis("off")
  
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 2 for calling a global function with example usage: plot_distribution(axes, X, y, hist_nbins, title, x0_label, x1_label) without return types