Complete the code as required $($You should not modify the code given as partial solution$)$:

$$

Agent implementation:

Initialize the agent attributes with the field health initialized as a random value between $60$ and $100.$

Define code to simulate agents movement by changing the agents pos assigning the value of new$\_$pos.

Update health values decreasing by $5$ on each step.

Use an appropriate method to record the agents health on each step.

Model implementation:

Create an object of type Grid using as height and width the size value in the initialization parameters. Assign this object to a model's field called room.

Create a list of $10$ agents of type MyAgent. Add them to the grid environment at random positions.

Update all agents health and position. If an agents health is $0,$ it must be removed from the grid.

Verify if there are healthy agents left in the room. If there are no agents left, stop the simulation.

Report the last positions of each agent using the$$end method of MyModel.

At the end of the execution, get the records of health for each agent and draw a plot.

Extra activity: Before removing an agent $($step $7),$ select its nearest neighbor and send a goodbye message. Add necessary methods to perform direct communication. Code: $!$pip install agentpy

# Model design

import agentpy as ap

import numpy as np

# Visualization

import matplotlib.pyplot as plt

import IPython class MyAgent$($ap$.$Agent$)$:

$$ def setup$($self$)$:

$$ # $1.$ Initialize agent's attribute: health

$$ # Setting initial position

$$ def setup$\_$pos$($self$,$ env$)$:

$$ self.pos $=$ env.positions$[$self$]$

$$ # Movement simulation

$$ def move$($self$)$:

$$ new$\_$pos $=($self$.$pos$[0]+$ self.model.random.randint$(-1,2),$

$$self$.$pos$[1]+$ self.model.random.randint$(-1,2))$

$$ # $2.$ Add code to simulate agents movement

$$ def update$($self$)$:

$$ # $3.$ Update health values

$$ # Updating position values using the move method

$$ self.move$()$

$$ # $4.$ Record health data

class MyModel$($ap$.$Model$)$:

$$ def setup$($self$)$:

$$ # $5.$ Initialize a grid with the given values

$$ # $6.$ Add $10$ agents of type MyAgent to the environment room at random position

$$ self.agents $=$ # Code for list of $10$ agents

$$ # Setting position in the pos field of the agents

$$ self.room.agents.setup$\_$pos$($self$.$room$)$

$$ def step$($self$)$:

$$ # $7.$ Update all agents values on each step

$$ # $7.$ Filter all agents with health $<=0.$ Remove them from the environment $($room$)$

$$ # Extra. Find the closest neighbor to the dying agent $($the one that will be removed$)$ And send a goodbye message with its id$.$

$$ # $8.$ Check if room has agents left. If no agents, stop the simulation

$$ def end$($self$)$:

$$ # $9.$ Report Agent's positions

$$ self.report$(\text{'}$Agents position', $[$agent$.$pos for agent in self.agents$])$parameters $=\{\text{'}$size$\text{'}$: $10\}$

model $=$ MyModel$($parameters$)$

results $=$ myModel.run$()$

# $10.$ Get the health results and last positions