[Python] Understanding backpropagation in PyTorch

I am exploring PyTorch, and I do not understand the output of the following example:

# Initialize x, y and z to values 4, -3 and 5
x = torch.tensor(4., requires_grad = True)
y = torch.tensor(-3., requires_grad = True)
z = torch.tensor(5., requires_grad = True)

# Set q to sum of x and y, set f to product of q with z
q = x + y
f = q * z

# Compute the derivatives
f.backward()

# Print the gradients
print("Gradient of x is: " + str(x.grad))
print("Gradient of y is: " + str(y.grad))
print("Gradient of z is: " + str(z.grad))


Output

Gradient of x is: tensor(5.)
Gradient of y is: tensor(5.)
Gradient of z is: tensor(1.)


I have little doubt that my confusion originates with a minor misunderstanding. Can someone explain in a stepwise manner?

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