import inspect
def func():
	'''
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.neural_network import MLPClassifier
import tensorflow as tf

X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, n_redundant=0, random_state=42)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

clf_svm = svm.SVC(kernel='linear', probability=True)
clf_svm.fit(X_train, y_train)
y_pred_svm = clf_svm.predict(X_test)
accuracy_svm = accuracy_score(y_test, y_pred_svm)
print(f"SVM Accuracy: {accuracy_svm:.2f}")

plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=plt.cm.coolwarm, label='Actual Data')

xx, yy = np.meshgrid(np.linspace(X_test[:, 0].min(), X_test[:, 0].max(), 100),
                     np.linspace(X_test[:, 1].min(), X_test[:, 1].max(), 100))

Z = clf_svm.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
Z = Z.reshape(xx.shape)

plt.contour(xx, yy, Z, levels=[0.5], linewidths=2, colors='black', linestyles='dashed', label='Decision Boundary')

plt.title('SVM Decision Boundary with Contour Line')
plt.legend()
plt.show()
'''
def px():
    code=inspect.getsource(func)
    print(code)
