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 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.out_features = w.shape[0]
      self.in_features = w.shape[1]
      self._packed_params.set_weight_bias(
          w, b, row_block_size, col_block_size
      )
  
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 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