def METHOD_NAME():
    A = te.placeholder((25, 100, 4), name="A")
    _A = tvm.tir.decl_buffer(A.shape, A.dtype, name="A")
    i = te.size_var("i")
    j = te.size_var("j")
    region = [tvm.ir.Range.from_min_extent(i[0], i[1]) for i in [(i, 2), (j, 8), (0, 4)]]
    stmt = tvm.tir.Prefetch(_A, region)
    func = tvm.te.schedule.SchedulePostProcToPrimFunc([_A], stmt, {A: _A})
    mod = tvm.IRModule.from_expr(func)
    mod = tvm.transform.Sequential(
        [tvm.tir.transform.StorageFlatten(64), tvm.tir.transform.Simplify()]
    )(mod)
    stmt = mod["main"].body
    assert stmt.extent.value == 2
    assert isinstance(stmt.body, tvm.tir.For)
    assert stmt.body.extent.value == 2
    def assert_flat_loads(stmt):
        if isinstance(stmt, tvm.tir.BufferLoad):
            assert len(stmt.indices) == 1, "All prefetch indices should be flattened"
    tvm.tir.stmt_functor.post_order_visit(stmt, assert_flat_loads)