Consider the following algorithm to compute the product of two matrices.

def matrix_product(A, B):
  mA = number of rows in A
  mB = number of rows in B
  nA = number of columns in A
  nB = number of columns in B

  if nA not equal mB:
    print("Matrix dimensions are not compatible for multiplication.")
    return NULL

  matrix_product = an mA x nB array
  for row in [0, 1, ..., mA - 1]:
    for col in [0, 1, ..., nB - 1]:
      myEntry = 0
      for index in [0, 1, ..., nA - 1]:
        myEntry = myEntry + (A[row, index] * B[index, col])

      matrix_product[row, col] = myEntry

  return matrix_product

Let `mA` denote the number of rows in matrix `A`, `nA` denote the number of columns in `A`, `mB` denote the number of rows in `B`, and `nB` denote the number of columns in `B`. Which expression best defines the run-time complexity of the matrix multiplication alrorithm, using big-O notation?