import inspect
def func():
	'''
import tensorflow as tf
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import matplotlib.pyplot as plt
 

sonar_data = pd.read_csv('Sonar.csv')
X = sonar_data.iloc[:, :-1].values
y = sonar_data.iloc[:, -1].values
label_encoder = LabelEncoder()
y = label_encoder.fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = tf.keras.models.Sequential([
    tf.keras.layers.Input(shape=(X_train.shape[1],)),
    tf.keras.layers.Dense(1, activation='sigmoid')
])
 

model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])
 

history = model.fit(X_train, y_train, epochs=100, verbose=1, validation_data=(X_test, y_test))
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=0)
print(f'Test Accuracy: {test_accuracy * 100:.2f}%')
 '''
def px():
    code=inspect.getsource(func)
    print(code)
