code: def dense_int8(cfg, data, weight, bias=None, out_dtype=None):
      """Dense operator for int8 on CUDA"""
      if out_dtype is None:
          out_dtype = data.dtype
  
      batch, in_dim = get_const_tuple(data.shape)
      out_dim, _ = get_const_tuple(weight.shape)
      k = te.reduce_axis((0, in_dim), name="k")
  
      matmul = te.compute(
          (batch, out_dim),
          lambda i, j: te.sum(
              data[i, k].astype(out_dtype)
              * weight[j, k].astype(out_dtype),
              axis=[k],
          ),
          tag="dense_int8",
      )
  
      cfg.add_flop(batch * in_dim * out_dim * 2)
  
      if bias is not None:
          matmul = te.compute(
              (batch, out_dim),
              lambda i, j: matmul[i, j] + bias[j].astype(out_dtype),
              tag=tag.BROADCAST,
          )
          cfg.add_flop(batch * out_dim)
  
      return matmul
  
file_path: tvm/python/tvm/topi/cuda/dense.py
imports: import logging
  import tvm
  from tvm import te, autotvm
  from tvm.contrib import cublas
  from .tensor_intrin import dp4a
  from ..None import tag
  from ..None import generic
  from ..utils import traverse_inline, get_const_tuple
label_desc: Use image node_id 6 for calling a global function with example usage: dense_int8(cfg, data, weight, bias, out_dtype) and returns: matmul