
    Design a fraud detection function that analyzes a sequence of transaction flags by examining temporal shifts in the pattern to identify suspicious behavior patterns. Given a binary sequence of normal (0) and suspicious (1) transaction flags and a number of time-shift positions, determine how many unique temporal views of the transaction pattern contain an odd number of suspicious flags. The analysis should help identify recurring fraud patterns across different time windows.
    

    
def odd_Equivalent(s, n):
    """
    Design a fraud detection function that analyzes a sequence of transaction flags by examining temporal shifts in the pattern to identify suspicious behavior patterns. Given a binary sequence of normal (0) and suspicious (1) transaction flags and a number of time-shift positions, determine how many unique temporal views of the transaction pattern contain an odd number of suspicious flags. The analysis should help identify recurring fraud patterns across different time windows.
    

    """
def odd_Equivalent(s, n):
    """
    Design a fraud detection function that analyzes a sequence of transaction flags by examining temporal shifts in the pattern to identify suspicious behavior patterns. Given a binary sequence of normal (0) and suspicious (1) transaction flags and a number of time-shift positions, determine how many unique temporal views of the transaction pattern contain an odd number of suspicious flags. The analysis should help identify recurring fraud patterns across different time windows.
    

    """
    count = 0
    for i in range(0, n):
        if s[i] == '1':
            count = count + 1
    return count