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DESCRIPTION OF SUSPECT #1 – Unknown male, 20s, heavy build, black hair, brown complexion, wearing a black shirt and blue jeans

DESCRIPTION OF SUSPECT #2 – Unknown female, 20s, medium build, black hair, tan complexion, tattoo down center of back, wearing a blue skirt and white halter top

DESCRIPTION OF SUSPECT #3 – Unknown male, 20s, heavy build, black hair, brown complexion, wearing a white shirt and white pants.

DESCRIPTION OF SUSPECT #4 – Unknown mixed Asian female, 40s, with straight black hair, wearing a black with a green print dress, long black sweater, and glasses on head.

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# https://github.com/pytorch/pytorch/issues/31285
pip3 install torch torchvision torchaudio --force-reinstall  --extra-index-url https://download.pytorch.org/whl/cu116

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import requests
import json

# Define your Azure app registration details
client_id = 'your_client_id'
client_secret = 'your_client_secret'
tenant_id = 'your_tenant_id'
authority = f"https://login.microsoftonline.com/{tenant_id}"
scope = ["https://graph.microsoft.com/.default"]
graph_url = 'https://graph.microsoft.com/v1.0/'

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from dataclasses import dataclass, field, asdict
from enum import Enum, auto

class Scale(Enum):
    NONE = 0
    LOW = 25
    MEDIUM = 50
    HIGH = 75
    MAX = 100

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from dataclasses import dataclass, field, fields, replace, asdict

_MISSING = object()

@dataclass
class Component(object):
    """
    Collection of attributes.
    """
    _key: str = field(default=None, hash=False, repr=False, compare=False)

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import cherrypy
import sqlalchemy
from sqlalchemy import Table, Column, ForeignKey, MetaData, Integer, String
from sqlalchemy.orm import scoped_session, sessionmaker, mapper, relationship
from sqlalchemy.orm.properties import ColumnProperty
from sqlalchemy.orm.util import object_mapper

# The base class from which all entities will extend
class BaseEntity(object):
	def __repr__(self):

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{
  "graph": [],
  "links": [
    {"source": 0, "target": 1},
    {"source": 0, "target": 2},
    {"source": 0, "target": 3},
    {"source": 0, "target": 4},
    {"source": 0, "target": 5},
    {"source": 0, "target": 6},
    {"source": 1, "target": 3},

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from sqlalchemy import Column, Integer, String, ForeignKey, create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship, backref, sessionmaker, joinedload

# For this example we will use an in-memory sqlite DB.
# Let's also configure it to echo everything it does to the screen.
engine = create_engine('sqlite:///:memory:', echo=True)

# The base class which our objects will be defined on.
Base = declarative_base()

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{"Dataset name": "Alerts", "Possible values": [], "Model": "Alerts", "Expected values": [], "Data type": "string", "Description": "The application involved in the event, such as win:app:trendmicro, vmware, nagios.", "Field name": "app"}
{"Dataset name": "Alerts", "Possible values": [], "Model": "Alerts", "Expected values": [], "Data type": "string", "Description": "The body of a message.", "Field name": "body"}
{"Dataset name": "Alerts", "Possible values": [], "Model": "Alerts", "Expected values": [], "Data type": "string", "Description": "The destination of the alert message, such as an email address or SNMP trap. You can alias this from more specific fields, such as dest_host, dest_ip, or dest_name.", "Field name": "dest"}
{"Dataset name": "Alerts", "Possible values": [], "Model": "Alerts", "Expected values": [], "Data type": "string", "Description": "The business unit associated with the destination. This field is automatically provided by asset and identity correlation features of applications like ClownS

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from django.test import TestCase

from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline

import numpy
import os