import inspect
def func():
	'''
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
from sklearn.metrics import mean_squared_error, mean_absolute_error,r2_score

data = pd.read_csv("Salary_Data.csv")

X = data["YearsExperience"].values.reshape(-1, 1)
y = data["Salary"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=42)
linear_model = LinearRegression()
linear_model.fit(X_train, y_train)
y_pred_linear = linear_model.predict(X_train)

poly_model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())
poly_model.fit(X_train, y_train)
y_pred_poly = poly_model.predict(X_train)

def calculate_metrics(y_true, y_pred):
    rmse = np.sqrt(mean_squared_error(y_true, y_pred))
    mae = mean_absolute_error(y_true, y_pred)
    r2 = r2_score(y_true, y_pred)
    return rmse, mae, r2

rmse_linear, mae_linear, r2_linear = calculate_metrics(y_train,y_pred_linear)
rmse_poly, mae_poly, r2_poly = calculate_metrics(y_train, y_pred_poly)
print("Linear Model:")
print(f'RMSE: {rmse_linear:.2f}')
print(f'MAE: {mae_linear:.2f}')
print(f'R2 Score: {r2_linear:.2f}')
print("Polynomial Model:")
print(f'RMSE: {rmse_poly:.2f}')
print(f'MAE: {mae_poly:.2f}')
print(f'R2 Score: {r2_poly:.2f}')

plt.figure(figsize=(12, 6))

plt.subplot(1, 2, 1)
plt.scatter(X_train, y_train, color='black', label='Actual')
plt.plot(X_train, y_pred_linear, color='blue', linewidth=3, label='Linear Model')
plt.title('Linear Regression Model')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.legend()

plt.subplot(1, 2, 2)
plt.scatter(X_train, y_train, color='black', label='Actual')
plt.plot(X_train, y_pred_poly, color='red', linewidth=3, label='Polynomial Model (degree=2)')
plt.title('Polynomial Regression Model')
plt.xlabel('Years of Experience')

plt.ylabel('Salary')
plt.legend()
plt.tight_layout()
plt.show()
 '''
def px():
    code=inspect.getsource(func)
    print(code)

