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 _load_from_state_dict(
      self,
      state_dict,
      prefix,
      local_metadata,
      strict,
      missing_keys,
      unexpected_keys,
      error_msgs,
  ):
      self.scale = float(state_dict[prefix + "scale"])
      state_dict.pop(prefix + "scale")
  
      self.zero_point = int(state_dict[prefix + "zero_point"])
      state_dict.pop(prefix + "zero_point")
  
      op_type = int(state_dict[prefix + "op_type"])
      state_dict.pop(prefix + "op_type")
  
      version = local_metadata.get("version", None)
      assert version <= self._version
  
      super()._load_from_state_dict(
          state_dict,
          prefix,
          local_metadata,
          False,
          missing_keys,
          unexpected_keys,
          error_msgs,
      )
  
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 7 for calling the Linear obj's underlying member method code with example usage: obj._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) without return types