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try:
    pincodefieldobj = WebDriverWait(browser, delay).until(EC.presence_of_element_located((By.XPATH, pincodefield)))
    pincodefieldobj.send_keys(pin)

    loginsubmitobj = WebDriverWait(browser, delay).until(EC.presence_of_element_located((By.XPATH, login_submit)))
    loginsubmitobj.click()

    age18p = WebDriverWait(browser, delay).until(EC.presence_of_element_located((By.XPATH, age18plus)))
    age18p.click()
    myElem = WebDriverWait(browser, delay).until(EC.presence_of_element_located((By.XPATH,databasepath)))

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opts = Options()
opts.add_argument('--headless')
browser = webdriver.Firefox(firefox_options=opts,executable_path='./geckodriver')
browser.get(website)
browser.find_element_by_xpath(searchbypin).click()

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from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.by import By
from selenium.common.exceptions import TimeoutException
from bs4 import BeautifulSoup
from selenium.webdriver.firefox.options import Options
import time
import pandas as pd
from call import *

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from twilio.rest import TwilioRestClient

def dial():
    TWILIO_PHONE_NUMBER = "<your twilio trial number>"
    number = "<your number that you have already verified>"
    TWIML_INSTRUCTIONS_URL ="https://gist.githubusercontent.com/thunderInfy/5fffbe366e990a2b057b872cfa49db73/raw/c8edb2b4ef044fb3806e3bb5ec459e08c1cecac1/twiml.xml"
    client = TwilioRestClient(<Account SID>, <AUTH TOKEN>)
    client.calls.create(to=number, from_=TWILIO_PHONE_NUMBER, url=TWIML_INSTRUCTIONS_URL, method="GET")

if __name__ == "__main__":

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<?xml version="1.0" encoding="UTF-8"?>
<Response>
    <Hangup/>
</Response>

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τ = 0.05

def loss_function(q, k, queue):

    # N is the batch size
    N = q.shape[0]
    
    # C is the dimensionality of the representations
    C = q.shape[1]

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# update resnetk
for θ_k, θ_q in zip(resnetk.parameters(), resnetq.parameters()):
    θ_k.data.copy_(momentum*θ_k.data + θ_q.data*(1.0 - momentum))

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# update the queue
queue = torch.cat((queue, k), 0) 

# dequeue if the queue gets larger than the max queue size - denoted by K
# batch size is 256, can be replaced by a variable
if queue.shape[0] > K:
    queue = queue[256:,:]

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# get loss value
loss = loss_function(q, k, queue)

# put that loss value in the epoch losses list
epoch_losses_train.append(loss.cpu().data.item())

# perform backprop on loss value to get gradient values
loss.backward()

# run the optimizer

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# zero out grads
optimizer.zero_grad()

# retrieve xq and xk the two image batches
xq = sample_batched['image1']
xk = sample_batched['image2']

# move them to the device
xq = xq.to(device)
xk = xk.to(device)