code: def plot_pca_2d_projection(
      clf,
      X,
      y,
      title="PCA 2-D Projection",
      ax=None,
      figsize=None,
      cmap="Spectral",
      title_fontsize="large",
      text_fontsize="medium",
  ):
      """Plots the 2-dimensional projection of PCA on a given dataset.
  
      Args:
          clf: Fitted PCA instance that can ``transform`` given data set into 2
              dimensions.
  
          X (array-like, shape (n_samples, n_features)):
              Feature set to project, where n_samples is the number of samples
              and n_features is the number of features.
  
          y (array-like, shape (n_samples) or (n_samples, n_features)):
              Target relative to X for labeling.
  
          title (string, optional): Title of the generated plot. Defaults to
              "PCA 2-D Projection"
  
          ax (:class:`matplotlib.axes.Axes`, optional): The axes upon which to
              plot the curve. If None, the plot is drawn on a new set of axes.
  
          figsize (2-tuple, optional): Tuple denoting figure size of the plot
              e.g. (6, 6). Defaults to ``None``.
  
          cmap (string or :class:`matplotlib.colors.Colormap` instance, optional):
              Colormap used for plotting the projection. View Matplotlib Colormap
              documentation for available options.
              https://matplotlib.org/users/colormaps.html
  
          title_fontsize (string or int, optional): Matplotlib-style fontsizes.
              Use e.g. "small", "medium", "large" or integer-values. Defaults to
              "large".
  
          text_fontsize (string or int, optional): Matplotlib-style fontsizes.
              Use e.g. "small", "medium", "large" or integer-values. Defaults to
              "medium".
  
      Returns:
          ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was
              drawn.
  
      Example:
          >>> import scikitplot.plotters as skplt
          >>> pca = PCA(random_state=1)
          >>> pca.fit(X)
          >>> skplt.plot_pca_2d_projection(pca, X, y)
          <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
          >>> plt.show()
  
          .. image:: _static/examples/plot_pca_2d_projection.png
             :align: center
             :alt: PCA 2D Projection
      """
      transformed_X = clf.transform(X)
      if ax is None:
          fig, ax = plt.subplots(1, 1, figsize=figsize)
  
      ax.set_title(title, fontsize=title_fontsize)
      classes = np.unique(np.array(y))
  
      colors = plt.cm.get_cmap(cmap)(np.linspace(0, 1, len(classes)))
  
      for label, color in zip(classes, colors):
          ax.scatter(
              transformed_X[y == label, 0],
              transformed_X[y == label, 1],
              alpha=0.8,
              lw=2,
              label=label,
              color=color,
          )
      ax.legend(
          loc="best",
          shadow=False,
          scatterpoints=1,
          fontsize=text_fontsize,
      )
      ax.set_xlabel("First Principal Component", fontsize=text_fontsize)
      ax.set_ylabel(
          "Second Principal Component", fontsize=text_fontsize
      )
      ax.tick_params(labelsize=text_fontsize)
  
      return ax
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import warnings
  import itertools
  import matplotlib.pyplot
  import numpy
  from sklearn.metrics import confusion_matrix
  from sklearn.preprocessing import label_binarize
  from sklearn.metrics import roc_curve
  from sklearn.metrics import auc
  from sklearn.metrics import precision_recall_curve
  from sklearn.metrics import average_precision_score
  from sklearn.utils.multiclass import unique_labels
  from sklearn.model_selection import learning_curve
  from sklearn.base import clone
  from sklearn.metrics import silhouette_score
  from sklearn.metrics import silhouette_samples
  from sklearn.utils import deprecated
  from scipy import interp
  from scikitplot.helpers import binary_ks_curve, validate_labels
label_desc: Use image node_id 10 for calling a global function with example usage: plot_pca_2d_projection(clf, X, y, title, ax, figsize, cmap, title_fontsize, text_fontsize) and returns: ax