code: def preprocess_csv(
      csv_in,
      csv_out,
      x_col="filename",
      y_col=None,
      sep=",",
      label_sep=" ",
      suffix="",
      split_by=None,
  ):
      """
      ```
      Takes a CSV where the one column contains a file name and a column
      containing a string representations of the class(es) like here:
      image_name,tags
      01, sunny|hot
      02, cloudy|cold
      03, cloudy|hot
  
      .... and one-hot encodes the classes to produce a CSV as follows:
      image_name, cloudy, cold, hot, sunny
      01.jpg,0,0,1,1
      02.jpg,1,1,0,0
      03.jpg,1,0,1,0
      Args:
          csv_in (str):  filepath to input CSV file
          csv_out (str): filepath to output CSV file
          x_col (str):  name of column containing file names
          y_col (str): name of column containing the classes
          sep (str): field delimiter of entire file (e.g., comma fore CSV)
          label_sep (str): delimiter for column containing classes
          suffix (str): adds suffix to x_col values
          split_by(str): name of column. A separate CSV will be
                         created for each value in column. Useful
                         for splitting a CSV based on whether a column
                         contains 'train' or 'valid'.
      Return:
          list :  the list of clases (and csv_out will be new CSV file)
      ```
      """
      if not y_col and not suffix:
          raise ValueError(
              "one or both of y_col and suffix should be supplied"
          )
      df = pd.read_csv(csv_in, sep=sep)
      f_csv_out = open(csv_out, "w")
      writer = csv.writer(f_csv_out, delimiter=sep)
      if y_col:
          df[y_col] = df[y_col].apply(str)
  
      # write header
      if y_col:
          classes = set()
          for row in df.iterrows():
              data = row[1]
              tags = data[y_col].split(label_sep)
              classes.update(tags)
          classes = list(classes)
          classes.sort()
          writer.writerow([x_col] + classes)
      else:
          classes = df.columns[:-1]
          write.writerow(df.columns)
  
      # write rows
      for row in df.iterrows():
          data = row[1]
          data[x_col] = data[x_col] + suffix
          if y_col:
              out = list(data[[x_col]].values)
              tags = set(data[y_col].strip().split(label_sep))
              for c in classes:
                  if c in tags:
                      out.append(1)
                  else:
                      out.append(0)
          else:
              out = data
          writer.writerow(out)
      f_csv_out.close()
      return classes
  
imports: from ..None import utils
  from ..imports import *
  from .preprocessor import ImagePreprocessor
label_desc: Use image node_id 12 for calling a global function with example usage: preprocess_csv(csv_in, csv_out, x_col, y_col, sep, label_sep, suffix, split_by) and returns: classes