code: def test_flatten_prefetch():
      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)
  
file_path: tvm/tests/python/tir-transform/test_tir_transform_storage_flatten.py
imports: import tvm
  import tvm.testing
  from tvm import te
  from tvm.driver.build_module import schedule_to_module
  from tvm.script import tir
  from tvm.relay import GlobalVar
label_desc: Use image node_id 2 for calling a global function with example usage: test_flatten_prefetch() without return types