docs: def plot_sample_rate_compare(data_dict, file_name=None):
      """
      -----
      Brief
      -----
      Is a plotting function that shows a sequence of ECG plots, demonstrating the relevance of
      choosing a wright sampling rate.
  
      -----------
      Description
      -----------
      Function intended to generate a Bokeh figure with Nx2 format, being N the number of keys of
      'data_dict' - one for each sampling rate (10, 100, 1000 Hz).
  
      At the first column is plotted a 10 seconds segment of the ECG signal at a specific
      sampling rate, while in the second column it will be presented a zoomed section of the ECG
      signal with 1 second, in order to the see the effect of choosing a specific sampling rate.
  
      Applied in the Notebook titled "Problems of a smaller sampling rate (aliasing)".
  
      ----------
      Parameters
      ----------
      data_dict : dict
          Dictionary that contains the data to be plotted
          ({"<sampling_rate_1>": {"time": <time_axis_1>, "data": <data_1>},
            "<sampling_rate_2>": {"time": <time_axis_2>, "data": <data_2>},
            ...})
  
      file_name : str
          Path containing the destination folder where the Bokeh figure will be stored.
  
      """
  
      # Generation of the HTML file where the plot will be stored.
      #file_name = _generate_bokeh_file(file_name)
  
      nbr_rows = len(list(data_dict.keys()))
  
      # List that store the figure handler.
      list_figures = plot([[]] * nbr_rows * 2, [[]] * nbr_rows * 2, y_axis_label="Raw Data",
                          x_axis_label="Time (s)", grid_lines=nbr_rows, grid_columns=2,
                          grid_plot=True, get_fig_list=True, show_plot=False)
  
      # Generation of Bokeh Figures.
      grid_list = []
      for iter, sample_rate in enumerate(list(data_dict.keys())):
          # List of figures in a valid gridplot format (each entry will define a row and each
          # subentry a column of the gridplot)
          grid_list += [[]]
  
          # Plotting of 10 seconds segment.
          # [Figure tile]
          title_unzoom = Title()
          title_unzoom.text = 'Sampling Rate: ' + sample_rate + " Hz"
          list_figures[2 * iter].title = title_unzoom
  
          # [Plot generation]
          list_figures[2 * iter].line(data_dict[sample_rate]["time"][:10 * int(sample_rate)],
                                      data_dict[sample_rate]["data"][:10 * int(sample_rate)],
                                      **opensignals_kwargs("line"))
  
          # Storage of customized figure.
          grid_list[-1] += [list_figures[2 * iter]]
  
          # Plotting of a zoomed section with 1 second.
          # [Figure tile]
          title_zoom = Title()
          title_zoom.text = 'Zoomed section @ ' + sample_rate + " Hz"
          list_figures[2 * iter + 1].title = title_zoom
  
          # [Plot generation]
          list_figures[2 * iter + 1].line(data_dict[sample_rate]["time"][:int(sample_rate)],
                                          data_dict[sample_rate]["data"][:int(sample_rate)],
                                          **opensignals_kwargs("line"))
  
          # Storage of customized figure.
          grid_list[-1] += [list_figures[2 * iter + 1]]
  
      # Organisation of figures in a gridplot.
      grid_plot_1 = gridplot(grid_list, **opensignals_kwargs("gridplot"))
  
      # Show representations.
      show(grid_plot_1)