code: def main(a: T.handle, d: T.handle) -> None:
      # function attr dict
      T.func_attr({"global_symbol": "main"})
      A = T.match_buffer(a, [1024, 1024], dtype="float32")
      D = T.match_buffer(d, [1024, 1024], dtype="float32")
      # body
      # with tir.block("root")
      B = T.alloc_buffer([1024, 1024], dtype="float32")
      for i0_0, i1_0, i0_1, i1_1 in T.grid(16, 64, 64, 16):
          with T.block("A"):
              vi = T.axis.S(1024, i0_0 * 64 + i0_1)
              vj = T.axis.S(1024, i1_0 * 16 + i1_1)
              T.reads([A[vi, vj]])
              T.writes([B[vi, vj]])
              B[vi, vj] = A[vi, vj] * T.float32(2)
      for i0_0, i1_0, i0_1, i1_1 in T.grid(16, 64, 64, 16):
          with T.block("C"):
              vi = T.axis.S(1024, i0_0 * 64 + i0_1)
              vj = T.axis.S(1024, i1_0 * 16 + i1_1)
              T.reads([B[vi, vj]])
              T.writes([D[vi, vj]])
              D[vi, vj] = (B[vi, vj] + T.float32(3)) * T.float32(5)
  
imports: import math
  import sys
  from typing import List
  import pytest
  import tvm
  import tvm.testing
  from tvm import te
  from tvm.ir.module import IRModule
  from tvm._ffi import register_func
  from tvm.error import TVMError
  from tvm.meta_schedule import TuneContext
  from tvm.meta_schedule.schedule_rule import PyScheduleRule
  from tvm.meta_schedule.space_generator import PostOrderApply
  from tvm.meta_schedule.utils import derived_object
  from tvm.script import tir
  from tvm.target import Target
  from tvm.tir.schedule import BlockRV, Schedule
label_desc: Use image node_id 1 for calling the TrinityMatmulProcessedForReference obj's underlying member method code with example usage: obj.main(a, d) without return types