Need help with creating the code for the Mnimax search. I am not sure how to write with the python language.

Write an adversarial agent in the provided MinimaxAgent class in multiAgents.py. Your minimax agent should work with any number of ghosts, so your algorithm should be a more generalized version of the standard Minimax algorithm that we have studied in the class. Your minimax tree will have multiple min layers (one for each ghost) for each max layer. Your code should also be able to expand the tree to an arbitrary depth which can be accessed from self.depth and score your nodes with the supplied self.evaluationFunction. Make sure your Minimax program refers to these variables since these are populated in response to the command line options. Important: A single search ply is considered to be one Pacman move and all the ghosts' responses, so depth 2 search will involve Pacman and each ghost moving two times. For further reading and understanding of Minimax (including alpha-beta pruning), please see this short video tutorial with pseudo code. Hints and Observations - Hint: Implement the algorithm recursively using helper function(s). - The correct implementation of minimax will lead to Pacman losing the game in some tests. This is not a problem: as it is correct behavior, it will pass the tests. - The evaluation function for the Pacman test in this part is implemented for you (self.evaluationFunction). You should not change this function but recognize that now we are evaluating states rather than actions, as compared to the reflex agent. Look-ahead agents evaluate future states whereas reflex agents evaluate actions from the current state