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def expand_and_evaluate(self, parent, action, child):
    if child.win is None:
        # it's either a non-terminal node or a terminal node that hasn't been
        # visited before

        # using controller (which knows the rules of the game) to get the next state
        next_state_obj, win = self.controller.get_next_state(parent.state, action)
        next_state = next_state_obj.get_array_view()
        if win is None:
            # not a terminal node

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# backward passes of results from MCTS is handled through recursion
def selection(self, node, root = False, logging = False, actions = None):

    # finds PUCT val for child nodes and returns the best child (the one with the max PUCT val)
    # and the best action for parent
    best_child, best_action = self.select_best_child(node, root)

    # ignore this logging part, this is not the core part of this function
    if(logging):
        if actions == None:

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class Node:
    def __init__(self, state, model):
        # saves state as a dictionary
        self.state = state
        # needs access to the neural network model
        self.model = model

        # W is the total reward and N is the number of playouts
        self.W = 0
        self.N = 0

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# if the current cell which is being handled has orbs of opposite color, then
# we need to decrease them from our opporbs count, 
# because they will change to our color
if state.color[row][col] == -turn_color:
    opporbs -= state.orbs[row][col]
    myorbs += state.orbs[row][col]

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'''
row and col are the indices for the event cell
the '1' in the following line of code indicates
that one orb would be added in that cell
'''
queue = [(row, col, 1)]

while len(queue)>0:
    row,col,val = queue.pop(0)

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# pygame graphics code part        
'''
Creates the pygame window
Does drawing functions like drawing the grid using rectangles
and drawing orbs using circles
The most important function here is the gameloop function
which takes the stateview dictionary and updates the pygame display.
(If you're familiar with p5.js, the gameloop function is like draw,
and __init__ is like setup,
except that we would manually call the gameloop function in a loop)

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'''
class for game state; stores the player's turn
stores number of orbs and color as numpy arrays, treats the color
of a vacant cell as 0, +1 if red and -1 if green.
get_array_view is the most important function here
which returns a dictionary having a board view and
the player's turn
'''
class State:
    def __init__(self, givenstate=None):

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data = None
if 'centers' in R.keys():
    data = R['centers']

if sum([j['available_capacity_dose1'] for i in data for j in i['sessions'] if j['min_age_limit'] <= 18]) > 0:
    dial()

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from datetime import date
import json
import time
import requests
from call import *

with open("data.json","r") as f:
    data = json.load(f)

url = "https://cdn-api.co-vin.in/api/v2/appointment/sessions/public/calendarByPin?pincode=%s&date=%s"%(data['pincode'], date.today().strftime("%d-%m-%Y"))

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def construct_df(soup):
    L = soup.findAll(True, {'class':['center-name-title','center-name-text', 'slots-box']})
    
    # obtaining class specific information from soup results
    
    M = []
    for i in L:
        class_names = i.get('class')
        if class_names == ['center-name-title']:
            ADD = i.get_text()