code: def train(task_type, feature_names, features, true_y):
      X_train = pd.DataFrame(data=features, columns=feature_names)
      y_train = np.array(list(true_y))
      pipeline = create_pipeline(X_train, task_type)
  
      model = pipeline.fit(X_train, y_train)
  
      def request_prediction(data):
          data = pd.DataFrame(data, columns=X_train.columns)
          if task_type == "classification":
              return json.dumps(
                  convert_to_list(model.predict_proba(data))
              )
          return json.dumps(convert_to_list(model.predict(data)))
  
      return request_prediction
  
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 3 for calling a global function with example usage: train(task_type, feature_names, features, true_y) and returns: request_prediction, json, json