import tensorflow as tf

# Define a TensorFlow graph

graph = tf.Graph()

with graph.as_default():

    x = tf.placeholder(tf.float32, shape=[None, 200])

    W = tf.Variable(tf.zeros([200, 10]))

    b = tf.Variable(tf.zeros([10]))

    y = tf.nn.softmax(tf.matmul(x, W) + b)

# Save the graph to disk

with tf.Session(graph=graph) as sess:

    tf.train.write_graph(sess.graph_def, './', 'graph.pb', as_text=False)

# Load the graph and create a converter

converter = tf.lite.TFLiteConverter.from_frozen_graph(

    graph_def_file='./graph.pb',

    output_arrays=['Softmax'],

    output_dtype=tf.float32.as_datatype_enum,

    inference_type=tf.lite.constants.QUANTIZED_UINT8,

    mean=[0.],

    std=[255.],

    optimizations=[tf.lite.Optimize.DEFAULT]

)

# Convert the graph to a TensorFlow Lite model

tflite_model = converter.convert()

# Save the TensorFlow Lite model to disk

with open('./model.tflite', 'wb') as f:

    f.write(tflite_model)

import tflite_runtime.interpreter as tflite

# Load the TensorFlow Lite model and create an interpreter

interpreter = tflite.Interpreter(model_path='./model.tflite', num_threads=4)

interpreter.allocate_tensors()

# Perform inference on a sample input

input_details = interpreter.get_input_details()

output_details = interpreter.get_output_details()

input_data = np.zeros(input_details[0]['shape'], dtype=np.float32)

interpreter