code: def plot_facet_grid(
      df, target, frow, fcol, tag="eda", directory=None
  ):
      r"""Plot a Seaborn faceted histogram grid.
  
      Parameters
      ----------
      df : pandas.DataFrame
          The dataframe containing the features.
      target : str
          The target variable for contrast.
      frow : list of str
          Feature names for the row elements of the grid.
      fcol : list of str
          Feature names for the column elements of the grid.
      tag : str
          Unique identifier for the plot.
      directory : str, optional
          The full specification of the plot location.
  
      Returns
      -------
      None : None.
  
      References
      ----------
  
      http://seaborn.pydata.org/generated/seaborn.FacetGrid.html
  
      """
  
      logger.info("Generating Facet Grid")
  
      # Calculate the number of bins using the Freedman-Diaconis rule.
  
      tlen = len(df[target])
      tmax = df[target].max()
      tmin = df[target].min()
      trange = tmax - tmin
      iqr = df[target].quantile(Q3) - df[target].quantile(Q1)
      h = 2 * iqr * (tlen ** (-1 / 3))
      nbins = math.ceil(trange / h)
  
      # Generate the pair plot
  
      sns.set(style="darkgrid")
  
      fg = sns.FacetGrid(df, row=frow, col=fcol, margin_titles=True)
      bins = np.linspace(tmin, tmax, nbins)
      fg.map(plt.hist, target, color="steelblue", bins=bins, lw=0)
  
      # Save the plot
      write_plot("seaborn", fg, "facet_grid", 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 14 for calling a global function with example usage: plot_facet_grid(df, target, frow, fcol, tag, directory) without return types