Write a Deep Q Learning $($DQN$)$ program code that is based on pytorch and can be executed in google colab. It is mainly used for speech emotion recognition.

Please replace the Q$-$network in DQN with SNN model.

The dataset part uses The Ryerson Audio$-$Visual Database of Emotional Speech and Song $($RAVDESS$),$ and then only selects the audio parts $($Audio$\_$Speech $,$ Audio$\_$Song$)$

After data preprocessing,

training dataset $=>$ x$\_$train: The size is $(2496,259),$ which means there are $2496$ pieces of data, and the length of each piece of data is $259.$

The label of the training dataset $=>$ y$\_$train: The size is $(2496,$ which means there are $2496$ labels.

test dataset $=>$ x$\_$test: The size is $(624,259),$ which means there are $624$ pieces of data, and the length of each piece of data is $259.$

The label of the test dataset $=>$ y$\_$test: The size is $(624,$ which means there are $624$ labels.

There are eight categories of Labels in total. Then only choose male to do it$.$