docstr: Calculate 2D coords to position the different methods in the main graph
  
  Parameters
  ----------
  mean_distances : DataFrame
      DataFrame storing all pairwise distances between methods
  
  Returns
  -------
  Coordinates of each method
docstr_tok: ["Calculate","2D","coords","to","position","the","different","methods","in","the","main","graph","Parameters","--","--","--","--","--","mean_distances",":","DataFrame","DataFrame","storing","all","pairwise","distances","between","methods","Returns","--","--","--","-","Coordinates","of","each","method"]
code: def calculate_coords(self, mean_distances):
      """
      Calculate 2D coords to position the different methods in the main graph
  
      Parameters
      ----------
      mean_distances : DataFrame
          DataFrame storing all pairwise distances between methods
  
      Returns
      -------
      Coordinates of each method
      """
      return MDS(
          n_components=2, dissimilarity="precomputed", random_state=0
      ).fit_transform(mean_distances)
  
imports: from category_encoders import OrdinalEncoder
  import copy
  import itertools
  import matplotlib.pyplot
  import numpy
  import pandas
  from plotly import graph_objs
  from plotly.offline import plot
  from plotly.subplots import make_subplots
  from sklearn.manifold import MDS
  from shapash import SmartExplainer
  from shapash.style.style_utils import colors_loading, select_palette, define_style
label_desc: Use image node_id 10 for calling the Consistency obj's underlying member method code with example usage: obj.calculate_coords(mean_distances) and returns: MDS