code: def conformal_plot_plotly(fig, df_cp_lo, df_cp_hi, plotting_backend):
      """Plot conformal prediction intervals and quantile regression intervals in one plot
  
      Parameters
      ----------
          fig : plotly.graph_objects.Figure
              Figure showing the quantile regression intervals
          df_cp_lo : dataframe
              dataframe containing the lower bound of the conformal prediction intervals
          df_cp_hi : dataframe
              dataframe containing the upper bound of the conformal prediction intervals
      """
      col_lo = df_cp_lo.columns
      trace_cp_lo = go.Scatter(
          name=f"cp_{col_lo[1]}",
          x=df_cp_lo["ds"],
          y=df_cp_lo[col_lo[1]],
          mode="lines",
          line=dict(color="red"),
      )
  
      col_hi = df_cp_hi.columns
      trace_cp_hi = go.Scatter(
          name=f"cp_{col_hi[1]}",
          x=df_cp_hi["ds"],
          y=df_cp_hi[col_hi[1]],
          mode="lines",
          line=dict(color="red"),
      )
  
      fig.add_trace(trace_cp_lo)
      fig.add_trace(trace_cp_hi)
      if plotting_backend == "plotly-static":
          fig = fig.show("svg")
      return fig
  
file_path: neural_prophet/neuralprophet/plot_forecast_plotly.py
imports: import logging
  import numpy
  import pandas
  from neuralprophet.plot_model_parameters_plotly import get_dynamic_axis_range
  from neuralprophet.plot_utils import set_y_as_percent
label_desc: Use image node_id 8 for calling a global function with example usage: conformal_plot_plotly(fig, df_cp_lo, df_cp_hi, plotting_backend) and returns: fig