content: 
  *** epilogue fusion
  
  import cutlass
  import torch
  
  # 1. Declare a basic GEMM
  plan = cutlass.op.Gemm(
      element=torch.float32,
      layout=cutlass.LayoutType.RowMajor
  )
  
  # 2. Define an epilogue as a Python function
  def my_epilogue(accum, alpha, C, beta, bias):
      D = torch.relu(alpha * accum + beta * C + bias)
      return D
  
  # 3. Define types and shapes of each EVT operand/output
  empty_mn = torch.empty(size=(m, n), dtype=torch.float32)
  empty_bias = torch.empty(size=(m, 1), dtype=torch.float32)
  examples_inputs = {
      "accum": empty_mn, "C": empty_mn, "D": empty_mn,
      "alpha": 1.0, "beta": 1.0, "bias": empty_bias
  }
  
  # 4. Construct the EVT and assign it to the GEMM
  plan.epilogue_visitor = cutlass.epilogue.trace(
      my_epilogue, examples_inputs
  )
  
  # 5. Compile and run the kernel
  # 这里假设A、B、C、D、bias已经正确定义并初始化
  A, B, C, D, bias =...
  visitor_args = {
      "alpha": 2.0, "beta": 0.0,
      "C": C, "D": D, "bias": bias
  }
  plan.run(A, B, C, D, visitor_args=visitor_args)