code: def random_square_mask(shape, fraction):
    
  
    mask = np.ones(shape)
  
    patch_area = shape[0]*shape[1]*fraction
    patch_dim = np.int(math.floor(math.sqrt(patch_area)))
    if patch_area == 0 or patch_dim == 0:
      return mask
  
    x = np.random.randint(shape[0] - patch_dim)
    y = np.random.randint(shape[1] - patch_dim)
  
    mask[x:(x + patch_dim), y:(y + patch_dim), :] = 0
  
    return mask
docs: Create a numpy array with specified shape and masked fraction.
  
    Args:
      shape: tuple, shape of the mask to create.
      fraction: float, fraction of the mask area to populate with `mask_scalar`.
  
    Returns:
      numpy.array: A numpy array storing the mask.