Figure 1: CIFAR-10 Dataset. Given the CIFAR10 dataset, implement the following networks and report the results of the test accuracy via screenshot. Make sure to provide necessary comments on your code. The dataset can be loaded from here: COLAB LINK HERE (a) Implement the following deep learning architecture and evaluate its test accuracy. network = models.Sequential ( ) network.add (layers.Dense (128, activation='sigmoid')) network.add (layers. Dense ( 64 , activation='sigmoid')) network. add (layers. Dense (10, activation='sigmoid')) network. compile(optimizer='sgd', loss='mse',metrics=['accuracy']) network.fit (X_train, y-train, epochs=20, batch_size=16) (b) Given that you are able to change the number of layers, number of neurons, activaiton functions, and the optimizer. Design your own architecture, provide your models code, and a screenshot of the highest test accuracy of your model. activation : ['sigmoid', 'tanh', 'relu'] optimzer : ['sgd', 'RMSprop', 'Adam'] (c) Provide a high-level description of your model. Moreover, what components had the biggest impact on your accuracy