code: def join_dfs_after_data_drop(predicted, df, merge=False):
      """Creates the intersection between df and predicted, removing any dates that have been imputed and dropped in
      NeuralProphet.predict().
  
      Parameters
      ----------
          df : pd.DataFrame
              dataframe containing column ``ds``, ``y`` with all data
          predicted : pd.DataFrame
              output dataframe of NeuralProphet.predict.
          merge : bool
              whether to merge predicted and df into one dataframe.
              Options
              * (default) ``False``: Returns separate dataframes
              * ``True``: Merges predicted and df into one dataframe
  
      Returns
      -------
          pd.DataFrame
              dataframe with dates removed, that have been imputed and dropped
      """
      df["ds"] = pd.to_datetime(df["ds"])
      predicted[predicted.columns[0]] = pd.to_datetime(
          predicted[predicted.columns[0]]
      )  # first column is not always named ds
      df_merged = pd.DataFrame()
      df_merged = pd.concat(
          [
              predicted.set_index(predicted.columns[0]),
              df.set_index(df.columns[0]),
          ],
          join="inner",
          axis=1,
      )
      if not merge:
          predicted = df_merged.iloc[:, :-1]
          predicted = predicted.rename_axis("ds").reset_index()
          df = df_merged.iloc[:, -1:]
          df = df.rename_axis("ds").reset_index()
          return predicted, df
      else:
          return df_merged.rename_axis("ds").reset_index()
  
imports: from __future__ import annotations
  import logging
  import math
  from collections import OrderedDict
  from dataclasses import dataclass
  from typing import TYPE_CHECKING, List, Optional, Tuple
  import numpy
  import pandas
label_desc: Use image node_id 35 for calling a global function with example usage: join_dfs_after_data_drop(predicted, df, merge) and returns: predicted,  df, df_merged