<jupyter_start><jupyter_text>This is my titleHere is some regular text.```import numpy as np```<jupyter_code>import sklearn
print(sklearn.__version__)
import numpy
print(numpy.__version__)
import scipy
print(scipy.__version__)
import matplotlib
print(matplotlib.__version__)
import pandas
print(pandas.__version__)
import torch
print(torch.__version__)
import seaborn
print(seaborn.__version__)
import wordcloud
print(wordcloud.__version__)
import bs4
print(bs4.__version__)
import requests
print(requests.__version__)
import theano
print(theano.__version__)
import networkx
print(networkx.__version__)
import cv2
print(cv2.__version__)
import gym
print(gym.__version__)
!ls
!ls sample_data
import json
json.loads(open('sample_data/anscombe.json').read())
a = 5
print(a)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.095, 'fraction_numerical': 0.00625, 'mean_word_length': 8.88888888888889, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '609769', 'license': 'MIT', 'n_tokens_mistral': 264, 'n_tokens_neox': 258, 'n_words': 61, 'path': '1. Introduction to TensorFlow/LZ/Colab Basics/TF2_0_Demo.ipynb', 'repo_name': 'AmirRazaMBA/TensorFlow-Certification', 'size': '800'}