code: def load_from_long_to_dataframe(
      full_file_path_and_name, separator=","
  ):
      """Load data from a long format file into a Pandas DataFrame.
  
      Parameters
      ----------
      full_file_path_and_name: str
          The full pathname of the .csv file to read.
      separator: str
          The character that the csv uses as a delimiter
  
      Returns
      -------
      DataFrame
          A dataframe with sktime-formatted data
      """
      data = pd.read_csv(
          full_file_path_and_name, sep=separator, header=0
      )
      # ensure there are 4 columns in the long_format table
      if len(data.columns) != 4:
          raise ValueError("dataframe must contain 4 columns of data")
      # ensure that all columns contain the correct data types
      if (
          not data.iloc[:, 0].dtype == "int64"
          or not data.iloc[:, 1].dtype == "int64"
          or not data.iloc[:, 2].dtype == "int64"
          or not data.iloc[:, 3].dtype == "float64"
      ):
          raise ValueError(
              "one or more data columns contains data of an incorrect type"
          )
  
      data = from_long_to_nested(data)
      return data
  
label_desc: Use image node_id 1 for calling a global function with example usage: load_from_long_to_dataframe(full_file_path_and_name, separator) and returns: data