code: def plot_partial_dependence(
      est,
      X,
      features,
      fnames,
      tag,
      n_jobs=-1,
      verbosity=0,
      directory=None,
  ):
      r"""Display a Partial Dependence Plot.
  
      Parameters
      ----------
      est : estimator
          The scikit-learn estimator for calculating partial dependence.
      X : numpy array
          The data on which the estimator was trained.
      features : list of int
          Feature numbers of ``X``.
      fnames : list of str
          The feature names to plot.
      tag : str
          Unique identifier for the plot
      n_jobs : int, optional
          The maximum number of parallel jobs.
      verbosity : int, optional
          The amount of logging from 0 (minimum) and higher.
      directory : str
          Directory where the plot will be stored.
  
      Returns
      -------
      None : None.
  
      References
      ----------
  
      http://scikit-learn.org/stable/auto_examples/ensemble/plot_partial_dependence.html#sphx-glr-auto-examples-ensemble-plot-partial-dependence-py
  
      """
  
      logger.info("Generating Partial Dependence Plot")
  
      # Plot partial dependence
  
      fig, axs = plot_partial_dependence(
          est,
          X,
          features,
          feature_names=fnames,
          grid_resolution=50,
          n_jobs=n_jobs,
          verbose=verbosity,
      )
      title = "Partial Dependence Plot"
      fig.suptitle(title)
      plt.subplots_adjust(
          top=0.9
      )  # tight_layout causes overlap with suptitle
  
      # Save the plot
      write_plot(
          model, "matplotlib", plt, "partial_dependence", tag, directory
      )
  
imports: from alphapy.estimators import get_estimators
  from alphapy.globals import BSEP, PSEP, SSEP, USEP
  from alphapy.globals import ModelType
  from alphapy.globals import Partition, datasets
  from alphapy.globals import Q1, Q3
  from alphapy.utilities import remove_list_items
  from bokeh.plotting import figure, show, output_file
  import itertools
  import logging
  import math
  import matplotlib
  import matplotlib.pyplot
  from mpl_toolkits.mplot3d import Axes3D
  import numpy
  import pandas
  from scipy import interp
  import seaborn
  from sklearn.calibration import calibration_curve
  from sklearn.inspection import partial_dependence
  from sklearn.inspection import plot_partial_dependence
  from sklearn.metrics import auc
  from sklearn.metrics import confusion_matrix
  from sklearn.metrics import roc_curve
  from sklearn.model_selection import cross_val_score
  from sklearn.model_selection import learning_curve
  from sklearn.model_selection import StratifiedKFold
  from sklearn.model_selection import train_test_split
  from sklearn.model_selection import validation_curve
  from sklearn.utils.multiclass import unique_labels
label_desc: Use image node_id 12 for calling a global function with example usage: plot_partial_dependence(est, X, features, fnames, tag, n_jobs, verbosity, directory) without return types