class_docstr: A quantized sparse linear module with quantized tensor as inputs and outputs.
class_docstr_tok: ["A","quantized","sparse","linear","module","with","quantized","tensor","as","inputs","and","outputs","."]
code: def set_weight_bias(
      self,
      w: torch.Tensor,
      b: Optional[torch.Tensor],
      row_block_size: Optional[int],
      col_block_size: Optional[int],
  ) -> None:
      assert row_block_size is not None and col_block_size is not None
      self._packed_params.set_weight_bias(
          w, b, row_block_size, col_block_size
      )
  
imports: from typing import Optional
  import torch
  from torch.ao.nn.quantized.modules.utils import _quantize_weight, _hide_packed_params_repr
label_desc: Use image node_id 11 for calling the Linear obj's underlying member method code with example usage: obj.set_weight_bias(w, b, row_block_size, col_block_size) without return types