class_docstr: SmartPlotter is a Bridge pattern decoupling plotting functions from SmartExplainer.
  The smartplotter class includes all the methods used to display graphics
  Each SmartPlotter method is easy to use from a Smart explainer object,
  just use the following syntax
  Attributes :
  explainer: object
      SmartExplainer instance to point to.
  Example
  --------
  >>> xpl.plot.my_plot_method(param=value)
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docstr: An auxiliary function to select the row of interest.
  Parameters
  ----------
  line: list
      A one element list containing the index of the observation of interest.
  var_dict: pandas.DataFrame
      A dataframe that indicates for each observation (each row)
      the index of the sorted contribution
      (sorted by descending order, in absolute values).
  x_val: pandas.DataFrame
      A dataframe with sorted features for each observation.
  contrib: pandas.DataFrame
      A dataframe with sorted contributions for each observation.
  Returns
  -------
  numpy arrays
      Unidimensional numpy arrays containing the values for one observation.
code: def get_selection(self, line, var_dict, x_val, contrib):
      """
      An auxiliary function to select the row of interest.
      Parameters
      ----------
      line: list
          A one element list containing the index of the observation of interest.
      var_dict: pandas.DataFrame
          A dataframe that indicates for each observation (each row)
          the index of the sorted contribution
          (sorted by descending order, in absolute values).
      x_val: pandas.DataFrame
          A dataframe with sorted features for each observation.
      contrib: pandas.DataFrame
          A dataframe with sorted contributions for each observation.
      Returns
      -------
      numpy arrays
          Unidimensional numpy arrays containing the values for one observation.
      """
      contrib = contrib.loc[line[0], :].values
      x_val = x_val.loc[line[0], :].values
      var_dict = var_dict.loc[line[0], :].values
  
      return var_dict, x_val, contrib
  
imports: import warnings
  from numbers import Number
  import random
  import copy
  import math
  import numpy
  from scipy.optimize import fsolve
  import scipy.cluster.hierarchy
  import pandas
  from plotly import graph_objs
  import plotly.express
  from plotly.subplots import make_subplots
  from plotly.offline import plot
  from shapash.manipulation.select_lines import select_lines
  from shapash.manipulation.summarize import compute_features_import, project_feature_values_1d, compute_corr
  from shapash.utils.utils import add_line_break, truncate_str, compute_digit_number, add_text, maximum_difference_sort_value, compute_sorted_variables_interactions_list_indices, compute_top_correlations_features
  from shapash.webapp.utils.utils import round_to_k
  from shapash.style.style_utils import colors_loading, select_palette, define_style
label_desc: Use image node_id 10 for calling the SmartPlotter obj's underlying member method code with example usage: obj.get_selection(line, var_dict, x_val, contrib) and returns: var_dict,  x_val,  contrib