code: def plot_scatter(df, features, target, tag="eda", directory=None):
      r"""Plot a scatterplot matrix, also known as a pair plot.
  
      Parameters
      ----------
      df : pandas.DataFrame
          The dataframe containing the features.
      features: list of str
          The features to compare in the scatterplot.
      target : str
          The target variable for contrast.
      tag : str
          Unique identifier for the plot.
      directory : str, optional
          The full specification of the plot location.
  
      Returns
      -------
      None : None.
  
      References
      ----------
  
      https://seaborn.pydata.org/examples/scatterplot_matrix.html
  
      """
  
      logger.info("Generating Scatter Plot")
  
      # Get the feature subset
  
      features.append(target)
      df = df[features]
  
      # Generate the pair plot
  
      sns.set()
      sns_plot = sns.pairplot(df, hue=target)
  
      # Save the plot
      write_plot("seaborn", sns_plot, "scatter_plot", 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 13 for calling a global function with example usage: plot_scatter(df, features, target, tag, directory) without return types