Random sample from specific rows and columns of a 2d numpy array (essentially sampling by ignoring edge effects)

I have a 2d numpy array size 100 x 100. I want to randomly sample values from the "inside" 80 x 80 values so that I can exclude values which are influenced by edge effects. I want to sample from row 10 to row 90 and within that from column 10 to column 90.

However, importantly, I need to retain the original index values from the 100 x 100 grid, so I can't just trim the dataset and move on. If I do that, I am not really solving the edge effect problem because this is occurring within a loop with multiple iterations.

gridsize = 100
new_abundances = np.zeros([100,100],dtype=np.uint8)
min_select = int(np.around(gridsize * 0.10))
max_select = int(gridsize - (np.around(gridsize * 0.10)))
row_idx =np.arange(min_select,max_select)
col_idx = np.arange(min_select,max_select)

indices_random = ????? Somehow randomly sample from new_abundances only within the rows and columns of row_idx and col_idx set.

What I ultimately need is a list of 250 random indices selected from within the new_abundances array.