code: def transformed_simple_compute_with_other_annotation(
      A: T.Buffer((16, 16), "float32"), C: T.Buffer((16, 16), "float32")
  ) -> None:
      for tx in T.thread_binding(0, 16, thread="threadIdx.x"):
          with T.block():
              T.reads([A[tx, 0:16]])
              T.writes([C[tx, 0:16]])
              B = T.alloc_buffer(
                  [2, 16, 1], dtype="float32", scope="shared"
              )
              with T.block():
                  T.reads([A[tx, 0]])
                  T.writes([B[0, tx, 0]])
                  B[0, tx, 0] = A[tx, 0] * T.float32(2)
              with T.block():
                  T.reads([A[tx, 1:16], B[0:2, tx, 0]])
                  T.writes([B[0:2, tx, 0], C[tx, 0:15]])
                  for i in T.serial(
                      0,
                      15,
                      annotations={"pragma_loop_partition_hint": True},
                  ):
                      with T.block():
                          T.reads([A[tx, i + 1]])
                          T.writes([B[(i + 1) % 2, tx, 0]])
                          B[(i + 1) % 2, tx, 0] = A[
                              tx, i + 1
                          ] * T.float32(2)
                      with T.block():
                          T.reads([B[i % 2, tx, 0]])
                          T.writes([C[tx, i]])
                          C[tx, i] = B[i % 2, tx, 0] + T.float32(1)
              with T.block():
                  T.reads([B[1, tx, 0]])
                  T.writes([C[tx, 15]])
                  C[tx, 15] = B[1, 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 10 for calling a global function with example usage: transformed_simple_compute_with_other_annotation(A, C) without return types