i need pygame visualization of my code:

import numpy as np
import random
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Constants
GAME_AREA_WIDTH = 1000
GAME_AREA_HEIGHT = 1000

# Random enemy movement
def update_enemy_position(enemy_pos, enemy_vel):
    new_pos_x = enemy_pos[0] + enemy_vel[0]
    new_pos_y = enemy_pos[1] + enemy_vel[1]
    return new_pos_x, new_pos_y

def random_velocity():
    speed = random.uniform(3, 10)
    angle = random.uniform(0, 2 * np.pi)
    vel_x = speed * np.cos(angle)
    vel_y = speed * np.sin(angle)
    return vel_x, vel_y

# Neural network
input_neurons = 9 # Including the enemy velocities (2 additional inputs)
output_neurons = 2

model = Sequential()
model.add(Dense(32, activation='relu', input_dim=input_neurons))
model.add(Dense(64, activation='relu'))
model.add(Dense(output_neurons))

model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])

# Input: bullet speed, bullet range, player x, player y, enemy x, enemy y, enemy velocity x, enemy velocity y
def desired_joystick_coords(player_pos, enemy_pos, enemy_vel, bullet_speed, bullet_range):
    time_to_hit = bullet_range / bullet_speed
    
    future_enemy_pos = enemy_pos
    for _ in range(int(time_to_hit)):
        future_enemy_pos = update_enemy_position(future_enemy_pos, enemy_vel)
        enemy_vel = random_velocity() # Update enemy velocity to create unpredictable movements

    joystick_x = future_enemy_pos[0] - player_pos[0]
    joystick_y = future_enemy_pos[1] - player_pos[1]
    return joystick_x, joystick_y

def generate_training_data(num_samples):
    training_data = []

    for _ in range(num_samples):
        bullet_speed = random.uniform(50, 200)
        bullet_range = random.uniform(100, 500)
        player_pos = (random.randint(0, GAME_AREA_WIDTH), random.randint(0, GAME_AREA_HEIGHT))
        enemy_pos = (random.randint(0, GAME_AREA_WIDTH), random.randint(0, GAME_AREA_HEIGHT))
        enemy_vel = random_velocity()
        step = random.randint(0, 100)

        desired_coords = desired_joystick_coords(player_pos, enemy_pos, enemy_vel, bullet_speed, bullet_range)
        input_data = (bullet_speed, bullet_range, player_pos[0], player_pos[1], enemy_pos[0], enemy_pos[1], enemy_vel[0], enemy_vel[1], step)
        output_data = desired_coords
        training_data.append((input_data, output_data))
    
    return training_data

# Generate and prepare training data
num_samples = 10000
raw_training_data = generate_training_data(num_samples)
X, y = zip(*raw_training_data)

# Train the neural network
model.fit(np.array(X), np.array(y), epochs=10000, validation_split=0.2)
model.save('my_model3.h5')