
    Given a dataset of n historical patient records that need to be processed through the hospital's machine learning system for readmission risk analysis, determine the largest batch size (as a power of 2) that can be used to process these records without exceeding the total number of available records. This batch size optimization will ensure efficient processing of patient data through the hospital's ML pipeline while maintaining system performance.
    

    
def highest_Power_of_2(n):
    """
    Given a dataset of n historical patient records that need to be processed through the hospital's machine learning system for readmission risk analysis, determine the largest batch size (as a power of 2) that can be used to process these records without exceeding the total number of available records. This batch size optimization will ensure efficient processing of patient data through the hospital's ML pipeline while maintaining system performance.
    

    """
def highest_Power_of_2(n):
    """
    Given a dataset of n historical patient records that need to be processed through the hospital's machine learning system for readmission risk analysis, determine the largest batch size (as a power of 2) that can be used to process these records without exceeding the total number of available records. This batch size optimization will ensure efficient processing of patient data through the hospital's ML pipeline while maintaining system performance.
    

    """
    res = 0
    for i in range(n, 0, -1):
        if i & i - 1 == 0:
            res = i
            break
    return res