code: def transformed_opaque_access(a: T.handle, b: T.handle) -> None:
      A = T.match_buffer(a, [1024])
      B = T.match_buffer(b, [1024])
      for i in T.serial(0, 8):
          with T.block():
              vi = T.axis.S(8, i)
              T.reads(A[vi * 128 : vi * 128 + 128])
              T.writes(B[vi * 128 : vi * 128 + 128])
              A_cache = T.alloc_buffer([1024])
              with T.block():
                  v = T.axis.S(8, vi)
                  T.reads([A[v * 128 : v * 128 + 128]])
                  T.writes([A_cache[v * 128 : v * 128 + 128]])
                  T.evaluate(
                      T.call_extern(
                          "test",
                          A_cache.data,
                          v * 128,
                          128,
                          A.data,
                          v * 128,
                          128,
                          dtype="float32",
                      )
                  )
              for j in T.serial(0, 128):
                  with T.block():
                      v = T.axis.S(1024, vi * 128 + j)
                      T.reads([A_cache[v]])
                      T.writes([B[v]])
                      B[v] = A_cache[v]
  
imports: import numpy
  import tvm
  import tvm.testing
  from tvm import te
  from tvm.script import tir
  from tvm import relay, tir
  from tvm.relay.backend.te_compiler import lower_to_primfunc
  from tvm.tir.tensor_intrin.hexagon import VRMPY_u8u8i32_INTRIN
label_desc: Use image node_id 9 for calling a global function with example usage: transformed_opaque_access(a, b) without return types