2. Sparse Matrix Transpose 10 11 12 13 Matrices can be easily represented as 2d lists of numbers in Python. For dense matrices (matrices with mostly nonzero elements), this is acceptable. Sparse matrices on the other hand have mostly Os, so this is relatively space-inefficient. Instead of a 2d list, we will represent a sparse matrix using a dictionary, which will map (row, col) tuples to the value at that position. We will also store the dimensions explicitly so we know the size of the sparse matrix 14 15 16 17 18 19 20 21 v 22 23 24 25 With this sparse matrix data format, write the function sparse_matrix_transpose(sm (that takes a sparse matrix and returns a new, transposed sparse matrix. As an example, we might have a matrix m: m = [ [0, 0, 10), [20, 0, 0] 3 26 27 28 29 30 31 32 33 34 35 36 37 38 m can be represented as as a sparse matrix sm like so: 39 40 sm = { "rows": 2, "cols": 3, Test R