def process_latents()

in models/generator.py [0:0]


    def process_latents(self, z):
        # output should be list with separate latent code for each conditional layer in the model
        # should be a list

        if isinstance(z, list):  # latents already in proper format
            pass
        elif z.ndim == 2:  # standard training, shape [B, ch]
            z = [z] * (self.n_layers + 4)
        elif z.ndim == 3:  # latent optimization, shape [B, n_latent_layers, ch]
            n_latents = z.shape[1]
            z = [z[:, i] for i in range(n_latents)]
        return z