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<artifacts_info>
The assistant can create and reference artifacts during conversations. Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity.

# Good artifacts are...
- Substantial content (>15 lines)
- Content that the user is likely to modify, iterate on, or take ownership of
- Self-contained, complex content that can be understood on its own, without context from the conversation
- Content intended for eventual use outside the conversation (e.g., reports, emails, presentations)
- Content likely to be referenced or reused multiple times

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qwe

1231e

https://ocw.mit.edu/courses/media-arts-and-sciences/mas-714j-technologies-for-creative-learning-fall-2009/readings/

http://sherryturkle.mit.edu/ https://anthropology.mit.edu/people/faculty/hector-beltran https://www.google.com/search?newwindow=1&biw=1072&bih=767&sxsrf=ALeKk00zMP4sVgOsfybcq6qi917JtCmLOw%3A1604994388949&ei=VEWqX4a0OZjr-QbRva2oAw&q=mit+hasts+publication&oq=mit+hasts+publication&gs_lcp=CgZwc3ktYWIQAzIHCCEQChCgATIHCCEQChCgAToECAAQRzoECCMQJzoKCAAQyQMQFBCHAjoFCCEQoAFQ6xFYgCBg8yBoAHADeACAAZcCiAHLEJIBBTAuNC42mAEAoAEBqgEHZ3dzLXdpesgBCMABAQ&sclient=psy-ab&ved=0ahUKEwiGrq2DvvfsAhWYdd4KHdFeCzUQ4dUDCA0&uact=5

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from __future__ import print_function
from keras.preprocessing.image import ImageDataGenerator
import numpy as np 
import os
import glob
import skimage.io as io
import skimage.transform as trans

Sky = [128,128,128]
Building = [128,0,0]

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%matplotlib inline
import matplotlib.pyplot as plt
def plot_decision_boundary(X, y, model, steps=1000, cmap='Paired'):
    """
    Function to plot the decision boundary and data points of a model.
    Data points are colored based on their actual label.
    """
    cmap = plt.get_cmap(cmap)
    
    # Define region of interest by data limits

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model = Sequential()
model = Sequential([
        Dense(1, activation='sigmoid', input_shape=(2,))
    ])
model.compile(loss='binary_crossentropy', optimizer='sgd',metrics=['accuracy'])
model.fit(x=np.array(x).reshape(200,2),y=y,batch_size=15,epochs=25)

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import Tkinter
import socket
#task1
class log():
    def __init__(self,root):
        self.lab1=Tkinter.Label(root1,text="Username",relief=Tkinter.FLAT)
        self.lab1.pack()
        self.window=root
        self.tusername = Tkinter.Entry(root1,bd=2,width=20)
        self.tusername.pack()

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def readData(Data):
	# here try to manually make xls file into a csv file and check python CSV library
	# Q: what kind of variable do you want to use to store the file? 
	# list? 1-d list? 2-d list? dictionary? set? 
	pass 
	varoefdata=None
	return varoefdata

def seperateData(data_var):
	# supposely you could use a dictionary

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import random
size = 1000
men = range(size)
men = [i for i in men]
women = range(size)
matched_men = set()
matched_women = set()

def avg(ar):
    if len(ar)>0:

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def answer(x,y,z):
    def allequal(L):
        a = [i for i in L if i == L[0]]
        return len(a)==len(L)
    def format(m,d,y):
        a=[m,d,y]
        for i in range(0,3):
            if a[i]<10:
                a[i]="0"+str(a[i])
            else: