code: def dynamic_compute(a_handle: T.handle, c_handle: T.handle):
      k = T.int32()
      A = T.match_buffer(a_handle, (16, k), "float32")
      C = T.match_buffer(c_handle, (16, k), "float32")
      for tx in T.thread_binding(0, 16, thread="threadIdx.x"):
          for i in T.serial(
              0,
              k,
              annotations={
                  "software_pipeline_stage": [0, 1],
                  "software_pipeline_order": [0, 1],
              },
          ):
              with T.block("compute"):
                  T.reads(A[tx, i])
                  T.writes(C[tx, i])
                  B = T.alloc_buffer(
                      (16, 1), dtype="float32", scope="shared"
                  )
                  with T.block():
                      T.reads(A[tx, i])
                      T.writes(B[tx, 0])
                      B[tx, 0] = A[tx, i] * T.float32(2)
                  with T.block():
                      T.reads(B[tx, 0])
                      T.writes(C[tx, i])
                      C[tx, i] = B[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 7 for calling a global function with example usage: dynamic_compute(a_handle, c_handle) without return types