code: def nan_preprocessing(df):
      # Separate and encode target variable as 0,1
      y = df.pop("RiskPerformance")
      enc = LabelEncoder()
      y = enc.fit_transform(y)
      # Remove rows where all data is missing
      y = y[(df > -9).any(axis=1)]
      df = df.loc[(df > -9).any(axis=1)]
      # Replace remaining special values with NaN
      df = df[df >= 0]
      df["RiskPerformance"] = y
      return df
  
file_path: AIX360/aix360/datasets/heloc_dataset.py
imports: import os
  import pandas
  import numpy
  from sklearn.preprocessing import LabelEncoder, OneHotEncoder
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 1 for calling a global function with example usage: nan_preprocessing(df) and returns: df