code: def dense_int8(cfg, data, weight, bias=None, out_dtype=None):
      """Compute for uint8 x int8 -> int32 dense"""
      if out_dtype is None:
          out_dtype = data.dtype
      assert len(weight.shape) == 4
      assert data.dtype == "uint8" and weight.dtype == "int8"
      _, _, n_inner, k_inner = get_const_tuple(weight.shape)  # out_dim
      assert n_inner == 16 and k_inner == 4
      return dense_int8_compute(cfg, data, weight, bias)
  
imports: from __future__ import absolute_import
  import tvm
  from tvm import autotvm, te
  from tvm.autotvm.task.space import SplitEntity
  from tvm.contrib import cblas, dnnl, mkl
  from tvm.target.x86 import get_simd_32bit_lanes
  from tvm.target.codegen import target_has_features
  from ..None import generic, tag
  from ..utils import get_const_tuple, traverse_inline
  from .tensor_intrin import acc_32x32_int32_sapphirerapids, dot_16x1x16_uint8_int8_int32, dot_32x128x32_u8s8s32_sapphirerapids
label_desc: Use image node_id 9 for calling a global function with example usage: dense_int8(cfg, data, weight, bias, out_dtype) and returns: dense_int8_compute