code: def _plot_top_pairs(
      features,
      target,
      *,
      types=None,
      scatter_alpha="auto",
      scatter_size="auto",
      feature_names=None,
      how_many=4,
      additional_axes=0,
      random_state=None,
  ):
      top_pairs = _find_scatter_plots_classification(
          features, target, how_many=how_many, random_state=random_state
      )
      if feature_names is None:
          feature_names = [
              "feature {}".format(i) for i in range(features.shape[1])
          ]
      fig, axes = _make_subplots(
          len(top_pairs) + additional_axes, row_height=4
      )
      for x, y, score, ax in zip(
          top_pairs.feature0,
          top_pairs.feature1,
          top_pairs.score,
          axes.ravel(),
      ):
          if types is not None:
              jitter_x = types.low_card_int_ordinal[feature_names[x]]
              jitter_y = types.low_card_int_ordinal[feature_names[y]]
          else:
              jitter_x = False
              jitter_y = False
          discrete_scatter(
              features[:, x],
              features[:, y],
              c=target,
              ax=ax,
              alpha=scatter_alpha,
              s=scatter_size,
              jitter_x=jitter_x,
              jitter_y=jitter_y,
          )
          ax.set_xlabel(
              _shortname(feature_names[x])
              + ("(jittered)" if jitter_x else "")
          )
          ax.set_ylabel(
              _shortname(feature_names[y])
              + ("(jittered)" if jitter_y else "")
          )
          ax.set_title("{:.3f}".format(score))
      return fig, axes
  
imports: import warnings
  import numpy
  import matplotlib.pyplot
  import matplotlib
  import seaborn
  import pandas
  from sklearn.feature_selection import mutual_info_regression, mutual_info_classif, f_classif
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import scale
  from sklearn.decomposition import PCA
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
  from sklearn.metrics import recall_score
  from ..preprocessing import detect_types, clean
  from .utils import _check_X_target_col, _get_n_top, _make_subplots, _short_tick_names, _shortname, _prune_category_make_X, find_pretty_grid, _find_scatter_plots_classification, class_hists, discrete_scatter, mosaic_plot, _find_inliers, pairplot, _get_scatter_alpha, _get_scatter_size, _find_categorical_for_regression, _prune_categories, _apply_eng_formatter
  from .sankey import plot_sankey
  from warnings import warn
label_desc: Use image node_id 6 for calling a global function with example usage: _plot_top_pairs(features, target) and returns: fig,  axes