code: def process_dates(data):
      """
      function to process dates
      :param data: dataframe
      :return dataframe with dates preprocessed
      """
      for df in data:
          df = data[df]
          datetime_cols = df.select_dtypes("datetime64")
          for col in datetime_cols:
              df[f"{col}_DayOfWeek"] = df[col].dt.day_name()
              df[f"{col}_Year"] = df[col].dt.year
              df[f"{col}_Month"] = df[col].dt.month_name()
              df[f"{col}_MonthDay"] = df[col].dt.day
  
              del df[col]
  
imports: import numpy
  import pandas
  from sklearn.compose import ColumnTransformer
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.pipeline import Pipeline
  from prince.ca import CA
  from nltk.stem import WordNetLemmatizer
  from nltk.tokenize import word_tokenize
  from nltk.corpus import stopwords
  from autocorrect import Speller
  from sklearn.model_selection import train_test_split
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import OneHotEncoder, StandardScaler, FunctionTransformer, LabelEncoder
  from pandas.api.types import is_numeric_dtype
  import warnings
label_desc: Use image node_id 4 for calling a global function with example usage: process_dates(data) without return types