code: def matmul(
      A: T.Buffer((128, 128), "float32"),
      B: T.Buffer((128, 128), "float32"),
      C: T.Buffer((128, 128), "float32"),
  ) -> None:
      for i, j, k in T.grid(128, 128, 128):
          with T.block("update"):
              vi, vj, vk = T.axis.remap("SSR", [i, j, k])
              with T.init():
                  C[vi, vj] = T.float32(0)
              C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
  
imports: import pytest
  import tvm
  import tvm.testing
  from tvm import te, tir
  from tvm.script import tir
  from tvm.tir.schedule.testing import assert_structural_equal_ignore_global_symbol, verify_trace_roundtrip
  from tvm.tir.tensor_intrin.arm_cpu import DP4A_INTRIN, ARM_DOT_4x4_i8_NEON_INTRIN, ARM_DOT_4x4_i8_SDOT_INTRIN
  from tvm.tir.tensor_intrin.rocm import AMDGPU_SDOT4_INTRIN
  from tvm.tir.tensor_intrin.x86 import VNNI_DOT_16x4_INTRIN, AVX512_DOT_16x4_INTRIN
  from tvm.tir.tensor_intrin.hexagon import VRMPY_u8u8i32_INTRIN, VDMPY_i16i16i32_INTRIN
label_desc: Use image node_id 8 for calling a global function with example usage: matmul(A, B, C) without return types