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(batch_size, input_dim)) # Train the generator g_loss = combined.train_on_batch(noise, valid) # Print progress print(f"Epoch {epoch+1}/{epochs} | D loss: {d_loss[0]} | G loss: {g_loss}") # Set hyperparameters epochs = 1000 batch_size = 32 # Build the combined GAN model