class_docstr: A dynamically quantized sparse linear module with float tensor as inputs and outputs.
class_docstr_tok: ["A","dynamically","quantized","sparse","linear","module","with","float","tensor","as","inputs","and","outputs","."]
code: def bias(self):
      return self._weight_bias()[1]
  
file_path: pytorch/torch/ao/nn/sparse/quantized/dynamic/linear.py
imports: from typing import Optional
  import torch
  import torch.ao.nn.intrinsic
  from torch.ao.nn.sparse.quantized import linear
  from torch.ao.nn.sparse.quantized.utils import LinearBlockSparsePattern
  from torch.ao.nn.quantized.modules.utils import _quantize_weight, _hide_packed_params_repr
label_desc: Use image node_id 10 for calling the Linear obj's underlying member method code with example usage: obj.bias() and returns: self