import inspect
def func():
	'''
import tensorflow as tf
from tensorflow.keras import layers, models
import matplotlib.pyplot as plt
import numpy as np

mnist_path = 'mnist.npz'

with np.load(mnist_path, allow_pickle=True) as data:
    train_images, train_labels = data['x_train'], data['y_train']
    test_images, test_labels = data['x_test'], data['y_test']

train_images, test_images = train_images / 255.0, test_images / 255.0

 

def build_model(optimizer):
    model = models.Sequential([
        layers.Flatten(input_shape=(28, 28)),
        layers.Dense(128, activation='relu'),
        layers.Dense(10)
    ])
    model.compile(optimizer=optimizer,
                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                  metrics=['accuracy'])
    return model

 

optimizers = ['adam', 'adagrad', 'rmsprop']

 

for optimizer in optimizers:
    model = build_model(optimizer)
    history = model.fit(train_images, train_labels, epochs=5, validation_data=(test_images, test_labels))
    plt.plot(history.history['loss'], label=optimizer)

plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
'''
def px():
    code=inspect.getsource(func)
    print(code)
