class_docstr: fp16 matrix multiplication kernel
  dtypes: fp16 x fp16 = fp16
code: def _ref_forward(
      A, B, ref_dtype=torch.float32, bias=None, activation=""
  ):
      C = torch.matmul(A.type(ref_dtype), B.type(ref_dtype))
      C = bias_add_activation(C, bias, activation)
      return C
  
imports: import torch
  import triton
  import os
  from filelock import FileLock
  import deepspeed.ops.transformer.inference.triton.triton_matmul_kernel
  import pickle
  from io import open
  import deepspeed
  from pathlib import Path
  import atexit
label_desc: Use image node_id 7 for calling the Fp16Matmul obj's underlying member method code with example usage: obj._ref_forward(A, B, ref_dtype, bias, activation) and returns: C