import inspect
def func():
	'''
from numpy import array, hstack
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt

def split_sequences(sequences, n_steps):
    X, y = list(), list()
    for i in range(len(sequences)):
        end_ix = i + n_steps
        if end_ix > len(sequences)-1:
            break
        seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :]
        X.append(seq_x)
        y.append(seq_y)
    return array(X), array(y)

in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90])
in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95])
out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))])

in_seq1 = in_seq1.reshape((len(in_seq1), 1))
in_seq2 = in_seq2.reshape((len(in_seq2), 1))
out_seq = out_seq.reshape((len(out_seq), 1))

dataset = hstack((in_seq1, in_seq2, out_seq))

n_steps = 3
X, y = split_sequences(dataset, n_steps)

n_input = X.shape[1] * X.shape[2]
X = X.reshape((X.shape[0], n_input))

n_output = y.shape[1]

model = Sequential()
model.add(Dense(100, activation='relu', input_dim=n_input))
model.add(Dense(n_output))
model.compile(optimizer='adam', loss='mse')

model.fit(X, y, epochs=2000, verbose=0)


x_input = array([[70, 75, 145], [80, 85, 165], [90, 95, 185]])
x_input = x_input.reshape((1, n_input))


yhat = model.predict(x_input, verbose=0)
print(yhat)

all_predictions = model.predict(X, verbose=0)

y_actual = y.reshape(-1, 1)


plt.figure(figsize=(10, 6))
plt.plot(y_actual, label='Actual')
plt.plot(all_predictions, label='Predicted', linestyle='--')
plt.title('Actual vs Predicted Outputs')
plt.ylabel('Output')
plt.xlabel('Sample')
plt.legend()
plt.show()
'''
def px():
    code=inspect.getsource(func)
    print(code)

