
    In a medical imaging system that analyzes tissue density patterns, develop an algorithm to calculate the number of unique ways that n distinct intensity levels can be arranged such that exactly m transitions from higher to lower intensity occur between adjacent pixels, which is crucial for identifying abnormal tissue patterns. This calculation helps establish statistical thresholds for automated disease detection in medical scans.
    

    
def eulerian_num(n, m):
    """
    In a medical imaging system that analyzes tissue density patterns, develop an algorithm to calculate the number of unique ways that n distinct intensity levels can be arranged such that exactly m transitions from higher to lower intensity occur between adjacent pixels, which is crucial for identifying abnormal tissue patterns. This calculation helps establish statistical thresholds for automated disease detection in medical scans.
    

    """
def eulerian_num(n, m):
    """
    In a medical imaging system that analyzes tissue density patterns, develop an algorithm to calculate the number of unique ways that n distinct intensity levels can be arranged such that exactly m transitions from higher to lower intensity occur between adjacent pixels, which is crucial for identifying abnormal tissue patterns. This calculation helps establish statistical thresholds for automated disease detection in medical scans.
    

    """
    if m >= n or n == 0:
        return 0
    if m == 0:
        return 1
    return (n - m) * eulerian_num(n - 1, m - 1) + (m + 1) * eulerian_num(n - 1, m)