code: def transformed_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"):
          with T.block():
              T.reads(A[tx, 0 : T.max(1, k)])
              T.writes(
                  C[tx, T.min(0, k - 1) : T.min(0, k - 1) + T.max(k, 1)]
              )
              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])
                  with T.block(""):
                      T.where(0 < k)
                      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 : 1 + (k - 1)], B[0:2, tx, 0])
                  T.writes(B[0:2, tx, 0], C[tx, 0 : k - 1])
                  for i in range(k - 1):
                      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[(k + 1) % 2, tx, 0])
                  T.writes(C[tx, k - 1])
                  with T.block(""):
                      T.where(1 <= k)
                      T.reads(B[(k + 1) % 2, tx, 0])
                      T.writes(C[tx, k - 1])
                      C[tx, k - 1] = B[(k + 1) % 2, 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 8 for calling a global function with example usage: transformed_dynamic_compute(a_handle, c_handle) without return types