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def rectified(x):
return max(0.0, x)
def custom_predict(input):
for key, layer in layers.items():
# print(layer)
config = layer["config"]
weights = layer["weights"]
biases = layer["biases"]
# print(config)
neurons = config["units"]
output = [0]*neurons
for n in range(neurons):
activation = 0
assert len(input) == len(weights[n])
for (i, w) in zip(input, weights[n]):
activation += i * w
activation += biases[n]
# print(activation)
if(config["activation"] == 'relu'):
output[n] = rectified(activation)
else:
output[n] = activation
input = output
return input
days = int(input())
for i in range(days):
temps = [float(x) for x in input().split()]
ans = custom_predict(temps[:24])
print(" ".join([format(x, ".1f") for x in ans]))
