code: def perform_inference(request_info):
      input_file_path = request_info["input"]
      columns = request_info["cols"]
      x_cols, y_col = columns["x"], columns["y"]
      reader = DataReader({}, input_file_path)
      input_data = reader.data_reader(error_checking=False)
      model_input = pd.DataFrame()
      for col in x_cols:
          if col in input_data:
              model_input[col] = input_data[col].astype("float64")
          else:
              raise Exception(
                  "Column " + str(col) + " not in input data"
              )
      preprocessing_input = model_input.copy()
      num_values = len(preprocessing_input.index)
      preprocessing_input[y_col] = [0.0 for i in range(num_values)]
      request_info["df"] = preprocessing_input
      request_info["preserve"] = True
      request_info = preprocess_module(request_info)
      x_values = request_info["df"]
      train_x, test_x = x_values["train"], x_values["test"]
      original_data = pd.concat([train_x, test_x])
      model_input = original_data.copy()
      if "pca-model" in request_info:
          pca_model = request_info["pca-model"]
          model_input = applyPCATransformation(pca_model, model_input)
      model = request_info["model"]
      result = model.predict(model_input)
      transformations = request_info["result-transforms"]
      if transformations is not None:
          if len(transformations) > 0:
              for transformer in reversed(transformations):
                  result = transformer.inverse_transform(result)
      output = request_info["initial-data"]
      output[y_col] = result
      return output
  
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 8 for calling a global function with example usage: perform_inference(request_info) and returns: output