code: def remove_duplicate_cols_in_dataset(df):
      df = copy.deepcopy(df)
      cols = df.columns.tolist()
      number_duplicates = df.columns.duplicated().astype(int).sum()
      if number_duplicates > 0:
          print(
              "Detected %d duplicate columns in dataset. Removing duplicates..."
              % number_duplicates
          )
          df = df.loc[:, ~df.columns.duplicated()]
      return df
  
imports: import pandas
  import numpy
  from itertools import combinations
  from sklearn.preprocessing import StandardScaler
  from category_encoders import HashingEncoder, SumEncoder, PolynomialEncoder, BackwardDifferenceEncoder
  from category_encoders import OneHotEncoder, HelmertEncoder, OrdinalEncoder, CountEncoder, BaseNEncoder
  from category_encoders import TargetEncoder, CatBoostEncoder, WOEEncoder, JamesSteinEncoder
  from category_encoders.glmm import GLMMEncoder
  from sklearn.preprocessing import LabelEncoder
  from category_encoders.wrapper import PolynomialWrapper
  from .encoders import FrequencyEncoder
  from .None import settings
  import pdb
  import copy
  import dask
  import dask.dataframe
label_desc: Use image node_id 3 for calling a global function with example usage: remove_duplicate_cols_in_dataset(df) and returns: df