code: def transformed_dag_interleaving(
      A: T.Buffer((16, 16), "float32"),
      B: T.Buffer((16, 16), "float32"),
      C: 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], B[tx, 0:16])
              T.writes(C[tx, 0:16])
              AS = T.alloc_buffer(
                  [16, 1], dtype="float32", scope="shared"
              )
              BS = T.alloc_buffer(
                  [16, 1], dtype="float32", scope="shared"
              )
              AL = T.alloc_buffer(
                  [2, 1, 1], dtype="float32", scope="local"
              )
              BL = T.alloc_buffer(
                  [2, 1, 1], dtype="float32", scope="local"
              )
              with T.block():
                  T.reads(A[tx, 0], B[tx, 0], AS[tx, 0], BS[tx, 0])
                  T.writes(
                      AS[tx, 0], BS[tx, 0], AL[0, 0, 0], BL[0, 0, 0]
                  )
                  with T.block():
                      T.reads(A[tx, 0])
                      T.writes(AS[tx, 0])
                      AS[tx, 0] = A[tx, 0] * T.float32(2)
                  with T.block():
                      T.reads(B[tx, 0])
                      T.writes(BS[tx, 0])
                      BS[tx, 0] = B[tx, 0] + T.float32(2)
                  with T.block():
                      T.reads(AS[tx, 0])
                      T.writes(AL[0, 0, 0])
                      AL[0, 0, 0] = AS[tx, 0]
                  with T.block():
                      T.reads(BS[tx, 0])
                      T.writes(BL[0, 0, 0])
                      BL[0, 0, 0] = BS[tx, 0]
              with T.block():
                  T.reads(
                      A[tx, 1:16],
                      B[tx, 1:16],
                      AS[tx, 0],
                      BS[tx, 0],
                      AL[0:2, 0, 0],
                      BL[0:2, 0, 0],
                  )
                  T.writes(
                      AS[tx, 0],
                      BS[tx, 0],
                      AL[0:2, 0, 0],
                      BL[0:2, 0, 0],
                      C[tx, 0:15],
                  )
                  for i in T.serial(15):
                      with T.block():
                          T.reads(A[tx, i + 1])
                          T.writes(AS[tx, 0])
                          AS[tx, 0] = A[tx, i + 1] * T.float32(2)
                      with T.block():
                          T.reads(B[tx, i + 1])
                          T.writes(BS[tx, 0])
                          BS[tx, 0] = B[tx, i + 1] + T.float32(2)
                      with T.block():
                          T.reads(AS[tx, 0])
                          T.writes(AL[(i + 1) % 2, 0, 0])
                          AL[(i + 1) % 2, 0, 0] = AS[tx, 0]
                      with T.block():
                          T.reads(BS[tx, 0])
                          T.writes(BL[(i + 1) % 2, 0, 0])
                          BL[(i + 1) % 2, 0, 0] = BS[tx, 0]
                      with T.block():
                          T.reads(AL[i % 2, 0, 0], BL[i % 2, 0, 0])
                          T.writes(C[tx, i])
                          C[tx, i] = AL[i % 2, 0, 0] * BL[i % 2, 0, 0]
              with T.block():
                  T.reads(AL[1, 0, 0], BL[1, 0, 0])
                  T.writes(C[tx, 15])
                  C[tx, 15] = AL[1, 0, 0] * BL[1, 0, 0]
  
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 14 for calling a global function with example usage: transformed_dag_interleaving(A, B, C) without return types