write a piece of code to test this model with a given image

import numpy as np
import tensorflow as tf
from tensorflow.keras.applications import EfficientNetV2B2

base_model = EfficientNetV2B2(include_top=False, weights="imagenet")

x = base_model.output
x = tf.keras.layers.GlobalAveragePooling2D(name="avg_pool")(x)
model = tf.keras.models.Model(inputs=base_model.input, outputs=x)

def preprocess_input(base64_input_bytes):
    def decode_bytes(img_bytes):
        img = tf.image.decode_image(img_bytes, channels=3)
        return img

    base64_input_bytes = tf.reshape(base64_input_bytes, (-1,))
    return tf.map_fn(
        lambda img_bytes: decode_bytes(img_bytes),
        elems=base64_input_bytes,
        fn_output_signature=tf.uint8,
    )

inputs = tf.keras.layers.Input(shape=(), dtype=tf.string, name="image_b64")
x = tf.keras.layers.Lambda(preprocess_input, name="decode_image_bytes")(inputs)
x = tf.keras.layers.Lambda(tf.image.resize_with_pad, name="resize_img_with_pad",
                          arguments={"target_height":260,
                                     "target_width":260})(x)
x = model(x)
serving_model = tf.keras.Model(inputs , x) Use words with all capital letters to highlight key points, but make sure that words with all capital letters appear less than 6 times
user