Complete algo function in following below code

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$\text{'}$: $(0,128,0),$ 'Wall': $(165,42,42),$ 'OurAgent': $(255,255,0)\})$def algo$($maze$)$: $"""$ implement this function using the maze from input $3$ in the maze represents the location of the agent $2$ represents the goal $1$ represents the wall $0$ represent the free space in which our agent can walk return and array of moves like this e$.$g$.["$down$",$ "right", $"$up$",$ "left", "left", "down"$]$ so that the agent will be at the goal use maze