MultiDataModel error during prediction: Please provide a model_fn implementation. about sagemaker-pytorch-inference-toolkit HOT 1 OPEN

**Note:** To directly use training job `model.tar.gz` outputs as we do here, you'll need to make sure your training job produces results that:  

    - Already include any required inference code in a `code/` subfolder, and\n",
    - (If you're using SageMaker PyTorch containers v1.6+) have been packaged to be compatible with TorchServe.\n",

See the `enable_sm_oneclick_deploy()` and `enable_torchserve_multi_model()` functions in [src/train.py](src/train.py) for notes on this. Alternatively, you can perform the same steps after the fact - to produce a new, serving-ready `model.tar.gz` from your raw training job result."

# pay attention to code_location argument!!
    estimator = SKLearn(
        entry_point=TRAINING_FILE,  # script to use for training job
        role=role,
        source_dir=SOURCE_DIR,  # Location of scripts
        instance_count=1,
        instance_type=TRAIN_INSTANCE_TYPE,
        framework_version="1.2-1",  # 1.2-1 is the latest version
        output_path=s3_output_path,  # Where to store model artifacts
        base_job_name=_job,
        code_location=code_location,  # This is where the .tar.gz of the source_dir will be stored
        metric_definitions=[{"Name": "median-AE", "Regex": "AE-at-50th-percentile: ([0-9.]+).*$"}],
        hyperparameters={"n-estimators": 100, "min-samples-leaf": 3, "model-name": location},
    )