the remaining part of the equation ( is equivalent to index ( )which indicates the number of elements to skip within the table as the number of dimensions increaseadditional products are added to the equationone for each new dimension for examplethe equation to compute the offset for - array is index ( ( ( ( you may notice pattern developing as the number of dimensions increase this pattern leads to general equation for computing the - array offset for element ( in within an -dimensional arrayindex( in in- fn- in ( where the fj values are the factors representing the number of elements to be skipped within the corresponding dimension and are computed using fn and fj dk < < ( = + the size of multi-dimensional array is fixed at the time it' created and cannot change during execution likewisethe several fj products used in the equation above will not change once the size of the array is set this can be used to our advantage to reduce the number of multiplications required to compute the element offsets instead of computing the products every time an element is accessedwe can compute and store the factor values and simply plug them into the equation when needed variable-length arguments the definition of the multiarray adt requires variable-length argument for the constructor and the two element access methods the number of arguments passed to each method is supposed to equal the number of dimensions in the array python functions and methods can be defined to accept variable number of argumentswhich is exactly what we need to implement the multiarray adt consider the following functionwhich accepts any number of arguments (assumed to be numerical in this exampleand then prints how many arguments were passed and the sum of those arguments