import inspect
def func():
	'''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import tensorflow as tf
from tensorflow import keras
from sklearn.metrics import confusion_matrix, accuracy_score
 

data = pd.read_csv('wheat-seeds.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
y_train = y_train - 1
y_test = y_test - 1
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
model = keras.Sequential([
    keras.layers.Input(shape=(X_train.shape[1],)),
    keras.layers.Dense(100, activation='relu'),
    keras.layers.Dense(3, activation='softmax')  # Assuming 3 classes in the dataset
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
history=model.fit(X_train, y_train, epochs=10, verbose=2)
loss, accuracy = model.evaluate(X_test, y_test)
print('Test loss:', loss)
print('Test accuracy:', accuracy)

import matplotlib.pyplot as plt
plt.figure(figsize=(10,6))
plt.plot(history.history['accuracy'],label='accuracy')
plt.xlabel("epochs")
plt.ylabel("accuracy")
plt.show()
'''
def px():
    code=inspect.getsource(func)
    print(code)

