code: def plot_coefficients(
      coefficients,
      feature_names,
      n_top_features=10,
      classname=None,
      ax=None,
  ):
      """Visualize coefficients of a linear model.
  
      Parameters
      ----------
      coefficients : nd-array, shape (n_features,)
          Model coefficients.
  
      feature_names : list or nd-array of strings, shape (n_features,)
          Feature names for labeling the coefficients.
  
      n_top_features : int, default=10
          How many features to show. The function will show the largest (most
          positive) and smallest (most negative)  n_top_features coefficients,
          for a total of 2 * n_top_features coefficients.
      """
  
      coefficients = coefficients.squeeze()
      feature_names = np.asarray(feature_names)
      if coefficients.ndim > 1:
          # this is not a row or column vector
          raise ValueError(
              "coefficients must be 1d array or column vector, got"
              " shape {}".format(coefficients.shape)
          )
      coefficients = coefficients.ravel()
  
      if len(coefficients) != len(feature_names):
          raise ValueError(
              "Number of coefficients {} doesn't match number of"
              "feature names {}.".format(
                  len(coefficients), len(feature_names)
              )
          )
      # get coefficients with large absolute values
      coef = coefficients.ravel()
      mask = coef != 0
      coef = coef[mask]
      feature_names = feature_names[mask]
      # FIXME this could be easier with pandas by sorting by a column
      interesting_coefficients = np.argsort(np.abs(coef))[
          -n_top_features:
      ]
      new_inds = np.argsort(coef[interesting_coefficients])
      interesting_coefficients = interesting_coefficients[new_inds]
      # plot them
      if ax is None:
          plt.figure(figsize=(len(interesting_coefficients), 5))
          ax = plt.gca()
      colors = [
          "red" if c < 0 else "blue"
          for c in coef[interesting_coefficients]
      ]
      ax.bar(
          np.arange(len(interesting_coefficients)),
          coef[interesting_coefficients],
          color=colors,
      )
      feature_names = np.array(feature_names)
      ax.set_xticks(np.arange(0, len(interesting_coefficients)))
      ax.set_xticklabels(
          feature_names[interesting_coefficients],
          rotation=60,
          ha="right",
      )
      _short_tick_names(ax, ticklabel_length=20)
      ax.set_ylabel("Coefficient magnitude")
      ax.set_xlabel("Feature")
      ax.set_title(classname)
      return feature_names[interesting_coefficients]
  
imports: from warnings import warn
  from functools import reduce
  import itertools
  import numpy
  import pandas
  import matplotlib.pyplot
  from matplotlib.axes import Axes
  from matplotlib.patches import Rectangle, Patch
  from matplotlib.ticker import EngFormatter
  from seaborn.utils import despine
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.metrics import roc_curve
  from sklearn.model_selection import cross_val_score, StratifiedShuffleSplit
  from ..preprocessing import detect_types
  from .._config import get_config
label_desc: Use image node_id 2 for calling a global function with example usage: plot_coefficients(coefficients, feature_names, n_top_features, classname, ax) and returns: feature_names