code: def transformed_three_stage_compute(
      A: T.Buffer((16, 16), "float32"), D: T.Buffer((16, 16), "float32")
  ) -> None:
      for tx in T.thread_binding(16, thread="threadIdx.x"):
          with T.block():
              T.reads(A[tx, 0:16])
              T.writes(D[tx, 0:16])
              B = T.alloc_buffer(
                  [2, 16, 1], dtype="float32", scope="shared"
              )
              C = T.alloc_buffer(
                  [2, 16, 1], dtype="float32", scope="shared"
              )
              with T.block():
                  T.reads(A[tx, 0:2], B[0:2, tx, 0])
                  T.writes(B[0:2, tx, 0], C[0:2, tx, 0])
                  for i in T.unroll(2):
                      with T.block():
                          T.reads(A[tx, i])
                          T.writes(B[0:2, tx, 0])
                          B[i, tx, 0] = A[tx, i] * T.float32(2)
                      with T.block():
                          T.where(i == 1)
                          T.reads(B[0:2, tx, 0])
                          T.writes(C[0:2, tx, 0])
                          C[(i + 1) % 2, tx, 0] = B[
                              (i + 1) % 2, tx, 0
                          ] + T.float32(2)
              with T.block():
                  T.reads(A[tx, 2:16], B[0:2, tx, 0], C[0:2, tx, 0])
                  T.writes(B[0:2, tx, 0], C[0:2, tx, 0], D[tx, 0:14])
                  for i in T.serial(14):
                      with T.block():
                          T.reads(A[tx, i + 2])
                          T.writes(B[0:2, tx, 0])
                          B[i % 2, tx, 0] = A[tx, i + 2] * T.float32(2)
                      with T.block():
                          T.reads(B[0:2, tx, 0])
                          T.writes(C[0:2, tx, 0])
                          C[(i + 1) % 2, tx, 0] = B[
                              (i + 1) % 2, tx, 0
                          ] + T.float32(2)
                      with T.block():
                          T.reads(C[0:2, tx, 0])
                          T.writes(D[tx, i])
                          D[tx, i] = C[i % 2, tx, 0] + T.float32(1)
              with T.block():
                  T.reads(B[0:2, tx, 0], C[0:2, tx, 0])
                  T.writes(C[0:2, tx, 0], D[tx, 14:16])
                  for i in T.unroll(2):
                      with T.block():
                          T.where(i < 1)
                          T.reads(B[0:2, tx, 0])
                          T.writes(C[0:2, tx, 0])
                          C[(i + 1) % 2, tx, 0] = B[
                              (i + 1) % 2, tx, 0
                          ] + T.float32(2)
                      with T.block():
                          T.reads(C[0:2, tx, 0])
                          T.writes(D[tx, i + 14])
                          D[tx, i + 14] = C[i, tx, 0] + T.float32(1)
  
imports: import sys
  import numpy
  import pytest
  import tvm
  import tvm.testing
  import tvm.tir.tensor_intrin.cuda
  from tvm import TVMError, te, tir
  from tvm.meta_schedule.testing import te_workload
  from tvm.script import tir
  from tvm.testing.tir import mma_schedule
  from tvm.tir.tensor_intrin.cuda import LDMATRIX_f16_A_DYN_INTRIN, LDMATRIX_f16_B_DYN_INTRIN, MMA_f16f16f32_INTRIN, MMA_fill_16x16_f32_INTRIN, MMA_store_16x16_f32_global_INTRIN, shared_16x16_to_ldmatrix_32x8_layout
label_desc: Use image node_id 12 for calling a global function with example usage: transformed_three_stage_compute(A, D) without return types