code: def __init__(
      self,
      input_features,
      output_features,
      bias=True,
      mp_group=None,
      compute_dtype=None,
  ):
      super().__init__(input_features, output_features, bias)
      self.in_len = input_features
      self.out_len = output_features
      self.weight_shape = (self.out_len, self.in_len)
      self.weight_length = self.out_len * self.in_len
      self.qtype = ggml_tensor_qtype["bf16"]
      self.mp_group = mp_group
      self.compute_dtype = compute_dtype
  
imports: from typing import Optional, TypeVar, Union, overload
  from bigdl.llm.utils.common import invalidInputError
  import os
  import torch
  import torch.nn.functional
  from torch import Tensor, device, dtype, nn
  from operator import mul
  from functools import reduce
  from bigdl.llm.transformers.xpu_customize_fwd import custom_fwd, custom_bwd
  from bigdl.llm.transformers.utils import get_autocast_dtype, get_xpu_device_type, get_ipex_version
  import bigdl.llm.ggml.model.llama.llama_cpp
  import ctypes
  from bigdl.llm.ggml.quantize import ggml_tensor_qtype
label_desc: Use image node_id 1 to create a new BF16Linear object from inherited base classes: nn with example: obj = BF16Linear(input_features, output_features, bias, mp_group, compute_dtype)