code: def input_fn(request_body, request_content_type):
      """
      The SageMaker XGBoost model server receives the request data body and the content type,
      and invokes the `input_fn`.
      Return a DMatrix (an object that can be passed to predict_fn).
      """
      if request_content_type == "text/libsvm":
          return xgb_encoders.libsvm_to_dmatrix(request_body)
      else:
          raise ValueError(
              "Content type {} is not supported.".format(
                  request_content_type
              )
          )
  
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 3 for calling a global function with example usage: input_fn(request_body, request_content_type) and returns: xgb_encoders