code: def predict_fn(input_data, model):
      """
      SageMaker XGBoost model server invokes `predict_fn` on the return value of `input_fn`.
      Return a two-dimensional NumPy array where the first columns are predictions
      and the remaining columns are the feature contributions (SHAP values) for that prediction.
      """
      prediction = model.predict(input_data)
      feature_contribs = model.predict(
          input_data, pred_contribs=True, validate_features=False
      )
      output = np.hstack((prediction[:, np.newaxis], feature_contribs))
      return output
  
file_path: sagemaker-python-sdk/tests/data/experiment/inference.py
imports: import os
  import json
  import logging
  import pickle
  import boto3
  import numpy
  import sagemaker_xgboost_container.encoder
  from sagemaker.session import Session
  from sagemaker.experiments import load_run
label_desc: Use image node_id 4 for calling a global function with example usage: predict_fn(input_data, model) and returns: output