It corresponds to the following code:
from math import e

unk = -1

# Activations
def h_act(x):
    return 1.0 / (1.0 + e**-x)
def y_act(x):
    return unk

# Inputs
x0 = 2
x1 = 3
x2 = 4

# Hidden layer weights and biases
h_w00 = 0.1
h_w01 = 0.5
h_w02 = 0.9
h_b0 = -2.0
h_w10 = unk
h_w11 = unk
h_w12 = unk
h_b1 = unk
h_w20 = unk
h_w21 = unk
h_w22 = unk
h_b2 = unk
h_w30 = unk
h_w31 = unk
h_w32 = unk
h_b3 = unk

# Output layer weights and biases
y_w00 = 1.3
y_w01 = unk
y_w02 = unk
y_w03 = unk
y_b0 = unk
y_w10 = unk
y_w11 = unk
y_w12 = unk
y_w13 = unk
y_b1 = unk

# Forward
h0 = h_act(h_w00 * x0 + h_w01 * x1 + h_w02 * x2 + h_b0)
h1 = h_act(h_w10 * x0 + h_w11 * x1 + h_w12 * x2 + h_b1)
h2 = h_act(h_w20 * x0 + h_w21 * x1 + h_w22 * x2 + h_b2)
h3 = h_act(h_w30 * x0 + h_w31 * x1 + h_w32 * x2 + h_b3)
y0 = y_act(y_w00 * h0 + y_w01 * h1 + y_w02 * h2 + y_w03 * h3 + y_b0)
y1 = y_act(y_w10 * h0 + y_w11 * h1 + y_w12 * h2 + y_w13 * h3 + y_b1)


To make it work properly, you have to change unk to the actual weights of the network. They are not given in the figure.