code: def __init__(
      self,
      in_features: int,
      out_features: int,
      bias: bool = True,
      device=None,
      dtype=None,
  ) -> None:
      factory_kwargs = {"device": device, "dtype": dtype}
      super().__init__()
      self.in_features = in_features
      self.out_features = out_features
      self.register_buffer(
          "weight",
          torch.empty((out_features, in_features), **factory_kwargs),
      )
      if bias:
          self.register_buffer(
              "bias",
              torch.empty(out_features, **factory_kwargs),
              persistent=False,
          )
      else:
          self.register_buffer("bias", None)
  
imports: import json
  import os
  import tempfile
  import unittest
  from collections import OrderedDict
  import torch
  import torch.nn
  from safetensors.torch import save_file
  from accelerate import init_empty_weights
  from accelerate.test_utils import require_cuda, require_huggingface_suite, require_multi_gpu
  from accelerate.utils.modeling import check_device_map, clean_device_map, compute_module_sizes, convert_file_size_to_int, find_tied_parameters, get_balanced_memory, infer_auto_device_map, load_checkpoint_in_model, load_state_dict, named_module_tensors, retie_parameters, set_module_tensor_to_device
label_desc: Use image node_id 1 to create a new LinearWithNonPersistentBuffers object from inherited base classes: nn with example: obj = LinearWithNonPersistentBuffers(in_features, out_features, bias, device, dtype)