wasi-nn: Support uint8 quantized networks (#2433)

Support (non-full) uint8 quantized networks.
Inputs and outputs are still required to be `float`. The (de)quantization is done internally by wasi-nn.

Example generated from `quantized_model.py`:
![Screenshot from 2023-08-07 17-57-05](https://github.com/bytecodealliance/wasm-micro-runtime/assets/80318361/91f12ff6-870c-427a-b1dc-e307f7d1f5ee)

Visualization with [netron](https://netron.app/).

core/iwasm/libraries/wasi-nn/test/models/quantized.py

@@ -0,0 +1,30 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import tensorflow as tf
+import numpy as np
+import pathlib
+
+model = tf.keras.Sequential([
+    tf.keras.layers.InputLayer(input_shape=[5, 5, 1]),
+    tf.keras.layers.AveragePooling2D(
+        pool_size=(5, 5), strides=None, padding="valid", data_format=None)
+
+])
+
+def representative_dataset():
+    for _ in range(1000):
+      data = np.random.randint(0, 25, (1, 5, 5, 1))
+      yield [data.astype(np.float32)]
+
+converter = tf.lite.TFLiteConverter.from_keras_model(model)
+converter.optimizations = [tf.lite.Optimize.DEFAULT]
+converter.representative_dataset = representative_dataset
+converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+converter.inference_input_type = tf.uint8  # or tf.int8
+converter.inference_output_type = tf.uint8  # or tf.int8
+tflite_model = converter.convert()
+
+tflite_models_dir = pathlib.Path("./")
+tflite_model_file = tflite_models_dir / "quantized_model.tflite"
+tflite_model_file.write_bytes(tflite_model)