code: def applyPCATransformation(pca_model, values):
      transformed = pca_model.transform(values)
      num_columns = transformed.shape[1]
      column_names = [
          "Principal#" + str(i + 1) for i in range(num_columns)
      ]
      transform_df = pd.DataFrame(transformed, columns=column_names)
      return transform_df
  
imports: from pandas.core.algorithms import mode
  from pandas.io import json
  from sklearn.metrics import accuracy_score
  from sklearn.linear_model import LinearRegression
  from sklearn.ensemble import RandomForestRegressor
  import os
  from sklearn.ensemble import BaggingClassifier
  import sys
  from importlib import import_module
  import shutil
  from sklearn.ensemble import VotingClassifier
  import ssl
  from nylon.preprocessing.preprocessing import initial_preprocessor
  import nltk
  import pandas
  import time
  from sklearn.model_selection import train_test_split
  from sklearn.decomposition import PCA
  from nylon.modeling.modeling import a_svm, nearest_neighbors, a_tree, sgd, gradient_boosting, adaboost, rf, mlp, default_modeling, svm_stroke, ensemble_stroke
  from nylon.preprocessing.preprocessing_methods import handle_scaling, handle_min_max, handle_label_encode, handle_ordinal, handle_filling, handle_importance, handle_one_hot, handle_text, handle_embedding, handle_dates
  from nylon.analysis.analysis import default_analysis, acc_score, cross_score, confusion, precision_calculation, recall_score_helper
  from nylon.data.reader import DataReader
label_desc: Use image node_id 2 for calling a global function with example usage: applyPCATransformation(pca_model, values) and returns: transform_df