docs: def plot_compare_resolutions(time, signal_res_1, signal_res_2, sampling_rate):
      """
      -----
      Brief
      -----
      With the current function it will be presented a gridplot with 2 rows, where three plots are graphically
      represented
  
      -----------
      Description
      -----------
      Acquiring data with a right resolution ensures that posterior conclusions will be more reliable and effective.
  
      The current function is intended to generate a Bokeh figure with 2 rows. In the first row is shown a plot
      containing two synchronized signals acquired with different resolutions.
  
      The second gridplot row is divided into 2 cells, showing each one the previously presented multi resolution
      signals but in separate subfigures and in a zoomed format.
  
      Applied in the Notebook titled "Resolution - The difference between smooth and abrupt variations".
  
      ----------
      Parameters
      ----------
      time : list
          Time-axis linked to the acquired signal samples.
  
      signal_res_1 : list
          Acquired signal samples for the smallest resolution.
  
      signal_res_2 : list
          Acquired signal samples for the biggest resolution.
  
      sampling_rate : int
          Sampling rate of the acquisition.
      """
  
      # Figure with the two plots
      figure_8_16 = figure(x_axis_label='Time (s)', y_axis_label="Temperature (ºC)", **opensignals_kwargs("figure"))
      figure_8_16.line(time, signal_res_1, legend="8 Bits Acquisition", **opensignals_kwargs("line"))
      figure_8_16.line(time, signal_res_2, legend="16 Bits Acquisition", **opensignals_kwargs("line"))
  
      # Zoom window
      wind_start = sampling_rate * 110  # 110 seconds
      wind_end = sampling_rate * 150  # 150 seconds
  
      # Figure with 8 bits zoom
      figure_8 = figure(x_axis_label='Time (s)', y_axis_label="Temperature (ºC)", title="8 Bits Acquisition",
                        y_range=[38, 40.5], **opensignals_kwargs("figure"))
      figure_8.line(time[wind_start:wind_end], signal_res_1[wind_start:wind_end], **opensignals_kwargs("line"))
  
      # Figure with 16 bits zoom
      figure_16 = figure(x_axis_label='Time (s)', y_axis_label="Temperature (ºC)", title="16 Bits Acquisition",
                         y_range=[38, 40.5], **opensignals_kwargs("figure"))
      figure_16.line(time[wind_start:wind_end], signal_res_2[wind_start:wind_end],
                     **opensignals_kwargs("line"))
  
      # Show gridplot.
      opensignals_style([figure_8, figure_16, figure_8_16])
      grid_plot = gridplot([[figure_8_16], [figure_8, figure_16]], **opensignals_kwargs("gridplot"))
      show(grid_plot)