Complete algo function in following below code after that perform these Implement the following uninformed search methods on the agent environment.i$.$ Breadth$-$first searchii. Depth$-$first searchiii. Uninformed search with the following cost function: left operation $=1,$ up operation $=2,$down operation $=3,$ right operation $=4$iv$.$ Greedy Best$-$First search with the Manhattan distance as a heuristic functionv. A$*$ search with the cost and heuristic functions as defined previously in UCS and GreedyBFS, respectively.a$.$ Make changes in the given code so that the user will specify the start and goal states.b$.$ For each algorithm, show the $($a$)$ time complexity, $($b$)$ space complexity, $($c$)$ complete pathfrom start to goal.c$.$ Make changes in the UI of agents environment so that all the explored paths are alsodisplayed as the agent is searching for the goal node. That is$,$ use a different color to showthe nodes placed on the fringe.from lib.agents import $*$import numpy as npclass Wall$($Thing$)$: passclass Goal$($Thing$)$: passfrom random import choiceclass OurAgent$($Agent$)$: location $=[0,1]$ direction $=$ Direction$("$down$")$ def moveforward$($self$,$ loc$)$: self.location$[0]+=$ loc$[0]$ self.location$[1]+=$ loc$[1]$ def turn$($self$,$ d$)$: self.direction $=$ Direction$($d$)$ def program$($percepts$)$: global it $\text{'}\text{'}\text{'}$Returns an action based on it's percepts'$\text{'}\text{'}$ for p in percepts: if isinstance$($p$,$ Wall$)$: print$("$Cannot move in a wall

Implement the algorithm again"$)$ it $+=1$ return steps$[$it$]$class Maze$($GraphicEnvironment$)$: def percept$($self$,$ agent$)$: $\text{'}\text{'}\text{'}$return a list of things that are in our agent's location'$\text{'}\text{'}$ things $=$ self.list$\_$things$\_$at$($agent$.$location$)$ for thing in self.list$\_$things$\_$at$($agent$.$location$)$: if not isinstance$($thing$,$ OurAgent$)$: things.append$($thing$)$ return things def execute$\_$action$($self$,$ agent, action$)$: $\text{'}\text{'}\text{'}$changes the state of the environment based on what the agent does.$\text{'}\text{'}\text{'}$ if action $==$ 'right': agent.moveforward$([1,0])$ elif action $==$ 'left': agent.moveforward$([-1,0])$ elif action $==\text{'}$up$\text{'}$: agent.moveforward$([0,-1])$ elif action $==$ 'down': agent.moveforward$([0,1])$ def is$\_$done$($self$)$: things $=[]$ for agent in self.agents: for thing in self.list$\_$things$\_$at$($agent$.$location$)$: things.append$($thing$)$ for thing in things: if isinstance$($thing$,$ Goal$)$: return True return Falsemaze$\_$layout $=[[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],[1,3,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1],[1,0,0,1,0,1,1,1,0,1,0,1,1,1,0,1,1,1,0,1],[1,0,0,1,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,1],[1,0,0,1,0,1,1,1,1,0,1,1,1,1,1,1,0,1,0,1],[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1],[1,1,1,1,1,1,0,0,1,0,1,1,1,1,1,1,1,1,1,1],[1,0,0,0,0,1,1,1,1,0,1,1,1,0,0,0,0,0,0,1],[1,0,1,1,0,0,0,0,1,0,0,1,0,0,1,1,1,0,0,1],[1,0,1,1,0,1,1,0,1,0,0,0,0,1,1,0,1,1,0,1],[1,0,0,0,0,1,1,0,1,0,1,1,0,1,1,0,0,0,0,1],[1,1,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1,1,1,1],[1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1],[1,1,1,1,1,0,1,1,1,1,1,1,0,1,1,0,1,1,0,1],[1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1],[1,0,1,0,1,1,1,1,1,1,0,1,1,0,1,1,0,1,0,1],[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1],[1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1],[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1],[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]]$maze$\_$instance $=$ Maze$(20,20,$ color$=\{\text{'}$Goal$\text{'}$: $(0,128,0),$

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