code: def timeit(f):
      @wraps(f)
      def wrapper(*args, **kwds):
          start_time = time.time()
          result = f(*args, **kwds)
          elapsed_time = time.time() - start_time
          print(f"Elapsed computation time: {elapsed_time:.3f} secs")
          return (elapsed_time, result)
  
      return wrapper
  
imports: from collections import Counter
  import numpy
  import pandas
  from sklearn.compose import ColumnTransformer
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, StandardScaler
  import os
  from tensorflow.keras.layers import Activation, BatchNormalization, Dense, Dropout
  from tensorflow.keras.models import Sequential
  import time
  from functools import wraps
  import tensorflow
  from sklearn.metrics import roc_auc_score
  from sklearn.utils import parse_version
  from imblearn.keras import BalancedBatchGenerator
  from sklearn.model_selection import StratifiedKFold
  import matplotlib.pyplot
  import seaborn
label_desc: Use image node_id 3 for calling a global function with example usage: timeit(f) and returns: wrapper, elapsed_time,  result