code: def split_label(dataset, target_feature):
      X = dataset.drop([target_feature], axis=1)
      y = dataset[[target_feature]]
      return X, y
  
file_path: responsible-ai-toolbox/apps/dashboard/src/model-assessment/model.py
imports: import json
  import numpy
  import pandas
  from sklearn.compose import ColumnTransformer
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import OneHotEncoder, StandardScaler
  from raiutils.data_processing import convert_to_list
label_desc: Use image node_id 1 for calling a global function with example usage: split_label(dataset, target_feature) and returns: X,  y