code: def __init__(self, dense_module, dtype):
      super().__init__()
      self.register_buffer(
          "weight", dense_module.weight.to_mkldnn(dtype)
      )
      if dense_module.bias is not None:
          # Bias can be fp32 or bf16 for OneDNN bf16 path, but for good accuracy,
          # we use fp32 dtype.
          self.register_buffer("bias", dense_module.bias.to_mkldnn())
      else:
          # TODO: Remove this once ScriptModule supports registering None buffer
          self.register_buffer(
              "bias",
              torch.zeros(
                  [dense_module.weight.size(0)], dtype=torch.float
              ).to_mkldnn(),
          )
  
imports: import torch
label_desc: Use image node_id 1 to create a new MkldnnLinear object from inherited base classes: torch.jit with example: obj = MkldnnLinear(dense_module, dtype)