human
Can you provide a the Python program you requested given the following:
2.3 (OR) Register a serialized version of the model using the VertaModelBase (Variation: take in a base64 encoded input vs. a tensor)
assistant
#Here is the Python program you requested.
class MNISTModel2(VertaModelBase):
    def __init__(self, artifacts):
        import tensorflow as tf
        import base64
        self.model = tf.keras.models.load_model(artifacts["mnist_model"])
    
    def predict(self, input_data):
        # decode base64
        import base64
        output = []
        for input_data_point in input_data:
            decoded_data = base64.b64decode(input_data_point["img_bytes"])
            decoded_data = tf.io.decode_image(decoded_data)
            decoded_data = tf.reshape(decoded_data, (1, 28, 28))
            output.append(self.model(decoded_data).numpy().tolist())
        return output

# test locally
import base64
mnist_model2 = MNISTModel2({"mnist_model" : "mnist.tf_saved_model/"})
with open("2.png", "rb") as image_file:
    encoded_string = base64.b64encode(image_file.read())
    print(mnist_model2.predict([{"img_bytes" : encoded_string}]))

model_version_from_cls_base64 = registered_model.create_standard_model(
    MNISTModel2, 
    environment=Python(["tensorflow"]), 
    name="v3", 
    artifacts={"mnist_model" : "mnist.tf_saved_model/"}
)