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// SPDX-License-Identifier: MIT
pragma solidity ^0.8.0;

interface IReentrance {
    function donate(address _to) external payable;
    function withdraw(uint _amount) external;
}

contract Attack {
    IReentrance public target;

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You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference

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// File: @openzeppelin/contracts/utils/introspection/IERC165.sol


// OpenZeppelin Contracts (last updated v5.1.0) (utils/introspection/IERC165.sol)

pragma solidity ^0.8.20;

/**
 * @dev Interface of the ERC-165 standard, as defined in the

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https://www.kaggle.com/c/FacebookRecruiting/discussion/2002

The provided  explanation is fine and all, but I will explain it differently here. (I would love to hear any improvements/corrections!)

Consider one row of test.csv. You have a source_node, and you have to predict an ordered list of up to ten destination_nodes. Facebook and Kaggle know, for that source_node, some number of correct destination_nodes.

For this row (one list of predictions) we do a sum and then divide to standardize.

For every prediction, is it correct? If it isn't correct, you get no points for that prediction. If it is correct, you get a number of points equal to the number of correct predictions up to and including this one, divided by the position of this prediction in the list. For example:

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import psycopg2

# For creating a context object. connection automatically closes.
# For joblib connection needs to be closed.
class DbConnector:
    
    def __init__(self):
        self.host = ""
        self.user = ""
        self.password = ""

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from sqlalchemy import create_engine

class DB_Connector:
    def __init__(self):
        #self.host='dcilda1832'
        self.host='pmlg012021'
        self.user='cdsem'
        self.password='Cdsem#1234'
        self.database='cdsem_qc'
    def __enter__(self):

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import numpy as np
import pylab as pl
from numpy import fft
    
def fourierExtrapolation(x, n_predict):
    n = x.size
    n_harm = 10                     # number of harmonics in model
    t = np.arange(0, n)
    p = np.polyfit(t, x, 1)         # find linear trend in x
    x_notrend = x - p[0] * t        # detrended x

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-- show running queries (pre 9.2)
SELECT procpid, age(clock_timestamp(), query_start), usename, current_query 
FROM pg_stat_activity 
WHERE current_query != '<IDLE>' AND current_query NOT ILIKE '%pg_stat_activity%' 
ORDER BY query_start desc;

-- show running queries (9.2)
SELECT pid, age(clock_timestamp(), query_start), usename, query 
FROM pg_stat_activity 
WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%' 

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#!/usr/bin/env/python

import psycopg2
import os
from io import StringIO
import pandas as pd

# Get a database connection
dsn = os.environ.get('DB_DSN')  # Use ENV vars: keep it secret, keep it safe
conn = psycopg2.connect(dsn)