def pre_inference(sample, signature, metadata):
    """Prepare a sample before it is passed into the model.
​
    Args:
        sample: The JSON request payload (parsed as a Python object).
​
        signature: Describes the expected shape and type of inputs to the model.
            If API model format is tensorflow: map<string, SignatureDef>
                https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/meta_graph.proto
            If API model format is onnx: list<onnxruntime.NodeArg>
                https://microsoft.github.io/onnxruntime/api_summary.html#onnxruntime.NodeArg
​
        metadata: Custom dictionary specified by the user in API configuration.
​
    Returns:
        A dictionary containing model input names as keys and Python lists or numpy arrays as values. If the model only has a single input, then a Python list or numpy array can be returned.
    """
    pass
​
def post_inference(prediction, signature, metadata):
    """Modify model output before responding to the request.
​
    Args:
        prediction: The output of the model.
​
        signature: Describes the output shape and type of outputs from the model.
            If API model format is tensorflow: map<string, SignatureDef>
                https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/meta_graph.proto
            If API model format is onnx: list<onnxruntime.NodeArg>
                https://microsoft.github.io/onnxruntime/api_summary.html#onnxruntime.NodeArg
​
        metadata: Custom dictionary specified by the user in API configuration.
​
    Returns:
        A Python dictionary or list.
    """

import numpy as np
​
labels = ["iris-setosa", "iris-versicolor", "iris-virginica"]
​
​
def pre_inference(sample, signature, metadata):
    # Convert the request payload to a flattened list
    return {
        signature[0].name: [
            sample["sepal_length"],
            sample["sepal_width"],
            sample["petal_length"],
            sample["petal_width"],
        ]
    }
​
def post_inference(prediction, signature, metadata):
    # Respond with a string label
    predicted_class_id = prediction[0][0]
    return labels[predicted_class_id]

boto3==1.9.228
msgpack==0.6.1
numpy==1.17.3
requests==2.22.0
dill==0.3.1.1
tensorflow==1.14.0  # For TensorFlow models only
onnxruntime==0.5.0  # For ONNX models only