code: def process_dates(data):
      for df in data:
          df = data[df]
          datetime_cols = df.select_dtypes("datetime64")
          for col in datetime_cols:
              if col in df.columns:
                  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]
  
file_path: nylon/nylon/preprocessing/preprocessing_inference.py
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.impute import SimpleImputer
  from sklearn.preprocessing import OneHotEncoder, StandardScaler, FunctionTransformer, LabelEncoder
label_desc: Use image node_id 3 for calling a global function with example usage: process_dates(data) without return types