code: def plot_ale(
      exp: Explanation,
      features: Union[List[Union[int, str]], Literal["all"]] = "all",
      targets: Union[List[Union[int, str]], Literal["all"]] = "all",
      n_cols: int = 3,
      sharey: str = "all",
      constant: bool = False,
      ax: Union["plt.Axes", np.ndarray, None] = None,
      line_kw: Optional[dict] = None,
      fig_kw: Optional[dict] = None,
  ) -> "np.ndarray":
      """
      Plot ALE curves on matplotlib axes.
  
      Parameters
      ----------
      exp
          An `Explanation` object produced by a call to the :py:meth:`alibi.explainers.ale.ALE.explain` method.
      features
          A list of features for which to plot the ALE curves or ``'all'`` for all features.
          Can be a mix of integers denoting feature index or strings denoting entries in
          `exp.feature_names`. Defaults to ``'all'``.
      targets
          A list of targets for which to plot the ALE curves or ``'all'`` for all targets.
          Can be a mix of integers denoting target index or strings denoting entries in
          `exp.target_names`. Defaults to ``'all'``.
      n_cols
          Number of columns to organize the resulting plot into.
      sharey
          A parameter specifying whether the y-axis of the ALE curves should be on the same scale
          for several features. Possible values are: ``'all'`` | ``'row'`` | ``None``.
      constant
          A parameter specifying whether the constant zeroth order effects should be added to the
          ALE first order effects.
      ax
          A `matplotlib` axes object or a `numpy` array of `matplotlib` axes to plot on.
      line_kw
          Keyword arguments passed to the `plt.plot` function.
      fig_kw
          Keyword arguments passed to the `fig.set` function.
  
      Returns
      -------
      An array of `matplotlib` axes with the resulting ALE plots.
  
      """
      import matplotlib.pyplot as plt
      from matplotlib.gridspec import GridSpec
  
      # line_kw and fig_kw values
      default_line_kw = {"markersize": 3, "marker": "o", "label": None}
      if line_kw is None:
          line_kw = {}
      line_kw = {**default_line_kw, **line_kw}
  
      default_fig_kw = {"tight_layout": "tight"}
      if fig_kw is None:
          fig_kw = {}
      fig_kw = {**default_fig_kw, **fig_kw}
  
      if features == "all":
          features = range(0, len(exp.feature_names))
      else:
          for ix, f in enumerate(features):
              if isinstance(f, str):
                  try:
                      f = np.argwhere(exp.feature_names == f).item()
                  except ValueError:
                      raise ValueError(
                          f"Feature name {f} does not exist."
                      )
              features[ix] = f
      n_features = len(features)
  
      if targets == "all":
          targets = range(0, len(exp.target_names))
      else:
          for ix, t in enumerate(targets):
              if isinstance(t, str):
                  try:
                      t = np.argwhere(exp.target_names == t).item()
                  except ValueError:
                      raise ValueError(
                          f"Target name {t} does not exist."
                      )
              targets[ix] = t
  
      # make axes
      if ax is None:
          fig, ax = plt.subplots()
  
      if isinstance(ax, plt.Axes) and n_features != 1:
          ax.set_axis_off()  # treat passed axis as a canvas for subplots
          fig = ax.figure
          n_cols = min(n_cols, n_features)
          n_rows = math.ceil(n_features / n_cols)
  
          axes = np.empty((n_rows, n_cols), dtype=object)
          axes_ravel = axes.ravel()
          # gs = GridSpecFromSubplotSpec(n_rows, n_cols, subplot_spec=ax.get_subplotspec())
          gs = GridSpec(n_rows, n_cols)
          for i, spec in zip(range(n_features), gs):
              # determine which y-axes should be shared
              if sharey == "all":
                  cond = i != 0
              elif sharey == "row":
                  cond = i % n_cols != 0
              else:
                  cond = False
  
              if cond:
                  axes_ravel[i] = fig.add_subplot(
                      spec, sharey=axes_ravel[i - 1]
                  )
                  continue
              axes_ravel[i] = fig.add_subplot(spec)
  
      else:  # array-like
          if isinstance(ax, plt.Axes):
              ax = np.array(ax)
          if ax.size < n_features:
              raise ValueError(
                  f"Expected ax to have {n_features} axes, got {ax.size}"
              )
          axes = np.atleast_2d(ax)
          axes_ravel = axes.ravel()
          fig = axes_ravel[0].figure
  
      # make plots
      for ix, feature, ax_ravel in zip(count(), features, axes_ravel):
          _ = _plot_one_ale_num(
              exp=exp,
              feature=feature,
              targets=targets,
              constant=constant,
              ax=ax_ravel,
              legend=not ix,  # only one legend
              line_kw=line_kw,
          )
  
      # if explicit labels passed, handle the legend here as the axis passed might be repeated
      if line_kw["label"] is not None:
          axes_ravel[0].legend()
  
      fig.set(**fig_kw)
      # TODO: should we return just axes or ax + axes
      return axes
  
imports: import copy
  import math
  import numpy
  import pandas
  from itertools import count
  from functools import partial
  from typing import Callable, List, Optional, Tuple, Union, Dict, TYPE_CHECKING, no_type_check
  import sys
  import logging
  from alibi.api.interfaces import Explainer, Explanation
  from alibi.api.defaults import DEFAULT_META_ALE, DEFAULT_DATA_ALE
label_desc: Use image node_id 6 for calling a global function with example usage: plot_ale(exp, features, targets, n_cols, sharey, constant, ax, line_kw, fig_kw) and returns: axes