Using a list passed into a Hidden Markov model, create a smoothing model with gamma $\mathrm{.}1$ and $\mathrm{.}001.$

Example list:

$[[(\text{'}$the$\text{'},$ 'det'$),(\text{'}$which$\text{'},$ 'det'$),(\text{'}$said$\text{'},$ 'verb'$),(\text{'}$jackson$\text{'},$ 'verb'$),(\text{'}$investigation$\text{'},$ 'verb'$),(\text{'}$action$\text{'},$ 'verb'$),(\text{'}$report$\text{'},$ 'noun'$),(\text{'}$in$\text{'},$ 'adj'$)]]$

Please feel free to bring in a different list to use here, but essentially a word and a grammatic tag.

Using that list, create a smoothing HMM function by using nltk LidstoneProbDist with the gamma given above.

Create an object of the HiddenMarkovModelTrainer passing in the list.

Lastly, use HiddenMarkovModelTrainer and ELE$\_$ProbDist from NLTK to train the dataset.