Does generator in conditonal GAN obey probability laws?

In probability, we have two types of probability functions: unconditional probability $$p(x)$$ and conditional probability $$p(x | y)$$. Both are fundamentally different and the latter can be obtained by the following equation

$$p(x|y) = \dfrac{p(x, y)}{p(y)} \text{ provided } p(y) \ne 0$$

I never heard of formal definition for conditioning except for conditional probability function.

But in case of neural networks, I came across the notion of conditioning.

$$\min_G \max_DV(D, G) = \mathbb{E}_{x ∼ P_{data}}[\log D(x|y)] + \mathbb{E}_{z ∼ p_z}[log (1 - D(G(z|y)))]$$

Since neural network $$D$$ is intended to implement a probability function, we can at-least think about conditioning on an input. But the neural network $$G$$ is not intended to implement probability function. $$G$$ is intended to provide datasamples by learning an underlying probability distribution whose output is not in the range $$[0, 1]$$.

Does $$G$$ obey the laws of probability? If yes, how, since its output is not restricted to $$[0, 1]$$? If no, then why the authors use the notation of conditional probability for $$G$$ also?

• Why would the output have to be in the range [0,1]? Who said G's output is a probability? Aug 5 at 10:36
• G is more like a function for sampling from a probability distribution Aug 5 at 10:36
• @user253751 Confusion is due to the notation of conditional probability $G(x|y)$. Aug 5 at 10:46
• @user253751 I didn't see any function till now of the form f(x|y) except conditional probability distribution function p(x|y). Aug 5 at 10:53

• So, the notation has to be $G(z,y)$, Then what might be the reason for using $G(z|y)$? Aug 5 at 12:13